Assay for pre-operative prediction of organ function recovery

ABSTRACT

Gene expression is measured in a sample of peripheral blood mononuclear cells (PBMCs) obtained from a subject and used to predict organ function recovery. A Function Recovery Potential (FRP) score is assigned to a sample that reflects the measured expression level of the genes identified herein in a direction associated with recovery from organ failure. Treatment of the subject with optimal medical management (OMM) and/or palliative care (PC) is advised when the FRP score is lower than the reference value, and referring the subject for treatment with therapies including—but not limited to—mechanical circulatory support (MCS) surgery, heart transplant (HTx) surgery, or other intervention for advanced heart failure is advised when the FRP score is greater than the reference value. A method for developing an FRP scoring algorithm that predicts a subject&#39;s ability to recover from medical intervention for organ failure is also described.

This application claims benefit of U.S. provisional patent applicationNo. 62/528,748, filed Jul. 5, 2017, and 62/595,383, filed Dec. 6, 2017,the entire contents of each of which are incorporated by reference intothis application.

ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT

This invention was made with Government support under HL120040, awardedby the National Institutes of Health. The Government has certain rightsin the invention.

REFERENCE TO A SEQUENCE LISTING SUBMITTED VIA EFS-WEB

The content of the ASCII text file of the sequence listing named“UCLA253_seq” which is 3 kb in size was created on Jul. 4, 2018, andelectronically submitted via EFS-Web herewith the application isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

In the United States, heart failure (HF) affects 6 million persons(Yancy 2013). HF with reduced ejection fraction (HFrEF) and HF withpreserved ejection fraction (HFpEF) each affect 3 million people. Thelifetime risk of developing HF is 1 in 5 for men and women older than 40years of age. The death rate remains unacceptably high at approximately50% within 5 years from the time of index diagnosis. In the US, anannually estimated 300,000 persons are diagnosed with Stage D heartfailure, also classified as advanced heart failure (AdHF) (Hunt 2009).

SUMMARY OF THE INVENTION

Described herein are methods and systems for treating a cardiovasculardisease. In some embodiments, described herein are methods and systemsfor predicting a prognosis of an individual with a cardiovasculardisease following the provision of a treatment to that individual. Insome embodiments, a prognosis of an individual following the provisionof a treatment to the individual is provided a score. In someembodiments, a treatment modality for an individual is selected based onthe score provided to the prognosis of the individual.

A large subgroup of patients with cardiovascular disease are patientswith heart failure (HF). Heart failure (HF) is a complex clinicalsyndrome that causes systemic hypo-perfusion and failure to meet thebody's metabolic demands. In an attempt to compensate, chronicupregulation of the sympathetic nervous system andrenin-angiotensin-aldosterone leads to further myocardial injury, HFprogression and reduced O2-delivery. This triggers progressive organdysfunction, immune system activation and profound metabolicderangements, creating a milieu similar to other chronic systemicdiseases and presenting as advanced HF (AdHF) with severely limitedprognosis.

In general, patients with AdHF may benefit from varioussurgical/interventional therapies such as mechanical circulatory support(MCS) surgery, heart transplant (HTx) surgery, coronary artery bypassgraft (CABG) surgery, percutaneous coronary interventions (PCI), aorticvalve replacement (AVR) surgery, mitral valve replacement (MVR) surgery,trans-catheter aortic valve replacement (TAVR), transcatheter mitralclip, ventricular tachycardia ablation, or stellate gangliectomy in lieuof optimal medical management (OMM) or palliative/hospice care (PC).While the Stage C HF guideline-based medical therapy is wellestablished, the survival benefit of these surgical/interventionaltherapeutic interventions is not as well established.

We hypothesize that 1-year survival in AdHF after thesesurgical/interventional therapies is linked to Functional RecoveryPotential (FRP), a novel clinical composite parameter that includes HFseverity, secondary organ dysfunction, comorbidities, frailty, anddisabilities as well as chronological age and that can be diagnosed by amolecular immunological biomarker.

HF is a major public health concern due to its tremendous societal andeconomic burden, with estimated direct and indirect cost in the U.S. of$37.2 billion in 2009, which is expected to increase to $97 billion by2030 (Roger 2012). While 25% of all spending occurs during the last yearof life (Orszag 2008, Zhang 2009) in patients hospitalized with HF, moreresource spending is associated with lower mortality rates (Ong 2009). Akey consideration is: Which of these AdHF surgical/interventionaltherapies does a healthcare provider recommend to the individualAdHF-patient in order to tailor personal benefits in the mostcost-effective way?

Across the different AdHF-interventions, the 1-year mortality rate is inthe range of 10-30% (Deng 2018). This ambiguity suggestsunpredictability of clinical trajectories, even with current clinicalprediction tools tailored to the progressive clinical trajectory of HFseverity and HF-related organ dysfunction (OD). Such models includeBrain Natriuretic Peptide (BNP) measurements (Troughton 2000, Gardner2003, Doust 2003), the Heart Failure Survival Score (HFSS) (Aaronson1997), Seattle Heart Failure Model (Levy 2006, Ketchum 2010), MAGGICscore (Sartipy 2014), Frailty Scores (Martinez-Selles 2009, Flint 2012),INTERMACS Score (Smits 2013, Kirklin 2014), UCLA score (Chyu 2014),Sequential Organ Failure Assessment (SOFA) Score (Vincent 1996),HeartMate II risk score (Cowger 2013), Model of End-stage Liver Disease(Matthews 2010), Model of End-stage Liver Disease Except INR (MELD-XI)Score (Abe 2014) and right ventricular failure score (Kormos 2010).However, most validated prediction tools have the tendency tounderestimate risk among the most severely ill patients. (Sartipy 2014).The uncertainty of predicting Stage D HF or AdHF-progression has animpact on individual patients' health and healthcare costs.

There remains a need for an improved prediction of risk associated witheach of the above treatment options for heart failure, and ultimately animproved prediction of risk reduction when choosing one treatment optionover another treatment option, i.e. an improved prediction ofcomparative survival benefit from the above interventions inAdHF-patients.

In some embodiments, the materials and methods described herein addressthese needs and more by using gene expression profiles to predict organfunction recovery. Described herein is, in a representative embodiment,a method of measuring gene expression in a sample of peripheral bloodmononuclear cells (PBMCs) obtained from a subject. The method can alsobe implemented as a method of predicting treatment outcome for advancedorgan failure, such as advanced heart failure, and as a method oftreating and/or a method of optimizing treatment of such organ failure.In some embodiments, the method comprises (a) measuring the expressionlevel of a set of at least 8 genes in the sample, wherein the at least 8genes are selected from those listed in Tables 2 and Table 3; (b)assigning a Function Recovery Potential (FRP) score to the sample thatreflects the measured expression level of the genes in a directionassociated with recovery from organ failure, wherein the FRP scorecorresponds to the measured expression level of the set of genesrelative to a reference value. In some embodiments, the subject issuffering from heart failure. Optionally, the method further comprises(c) treating the subject with optimal medical management (OMM) and/orpalliative care (PC) when the FRP score is lower than the referencevalue, and referring the subject for treatments including—but notlimited to—mechanical circulatory support (MCS) surgery, hearttransplant (HTx) surgery, coronary artery bypass graft (CABG) surgery,percutaneous coronary interventions (PCI), aortic valve replacement(AVR) surgery, mitral valve replacement (MVR) surgery, trans-catheteraortic valve replacement (TAVR), transcatheter mitral clip, ventriculartachycardia ablation, or stellate gangliectomy when the FRP score isgreater than the reference value. In some embodiments, the expressionlevel of 10-75 genes is measured. Alternatively, the expression level of10-30 genes is measured. In other embodiments, the expression level of10-15 genes is measured. In some embodiments, the set of genes is atleast 10 of the genes listed in Table 2, at least 10 of the genes listedin Table 3 or at least 10 of the genes listed in Table 4, or comprisesone gene selected from each of Tables 1A-1I. Optionally, the methodfurther comprises measuring one or more control genes.

In some embodiments, the reference value corresponds to expressionlevels of the set of genes observed in subjects who recover from heartfailure and/or major organ dysfunction. In one embodiment (see Bondar2017), the reference value is constituted by averaging the GEP valuesacross the 28 genes identified after 1) creating a dichotomous phenotypeframework defined as Group I (High FRP=17 HF-patients who had a goodfunctional recovery, as defined by improvement of Sequential OrganFailure Assessment (SOFA) score and Model of Endstage Liver Diseaseexcept INR (MELD-XI) score on day 8 after MCS surgery in comparison today −1 before MCS-surgery) versus Group II (Low FRP=12 patients how didnot fulfill this clinical criterion), 2) filtering the entire set ofmRNA transcripts (36,938) (20th-100th percentile), retaining of theresulting 26,571 entities only those with a fold change of at least 2.0(123 transcripts) for statistical analysis with the unpairedMann-Whitney test and Benjamini-Hochberg correction analysis (FDR=0.1),identifying 28 genes as differentially expressed between GROUP I andGROUP II on day −1 and 3) building a model using the support vectormachine (SVM) algorithm by randomly selecting 20 samples out of 29total, stratified by membership in Group I versus Group II. To test themodel, the remaining 9 samples were stratified by membership in Group Ior Group II. An average prediction accuracy of 93% (range: 78-100%) wasachieved after re-running the stratified random selection model buildingprocess 25 times. Thus, for any new HF-patient's blood sample with anunknown level of gene expression in the 28 genes listed in Table 3, theimputation of expression value of the 28 genes yields a dichotomousdecision whether this sample is to be allocated to Group I or Group IIwith an accuracy of 93% and therefore allows with an accuracy of 93% topredict before the scheduled HF-=intervention whether this newHF-patient has a high FRP (GROUP I) or low FRP (GROUP II) (see twopatient examples in FIG. 8). With this information added to the otherclinical data available to this patient's doctor, the doctor can make amore precisely informed recommendation to the patient about whether ornot to undergo the scheduled HF-intervention. In some embodiments suchas the example above, the subject is suffering from HF and the methodcomprises (c) treating the subject with optimal medical management (OMM)and/or palliative care (PC) when the FRP score is low and referring thesubject for treatments including—but not limited to—mechanicalcirculatory support (MCS) surgery, heart transplant (HTx) surgery,coronary artery bypass graft (CABG) surgery, percutaneous coronaryinterventions (PCI), aortic valve replacement (AVR) surgery, mitralvalve replacement (MVR) surgery, trans-catheter aortic valve replacement(TAVR), transcatheter mitral clip, ventricular tachycardia ablation, orstellate gangliectomy when the FRP is high. In other embodiments, theexpression level of 10-30 genes is measured. In some embodiments, theset of genes is at least 10 of the genes listed in Table 2, at least 10of the genes listed in Table 3 or at least 10 of the genes listed inTable 4A and 4B, or comprises one gene selected from each of Tables1A-1I. In some embodiments, the reference value corresponds toexpression levels of the set of genes observed in subjects who recoverfrom heart failure and/or major organ dysfunction. Optionally, themethod further comprises measuring one or more control genes.

In one representative embodiment, the FRP score is between 1 (lowest)and 10 (highest), and the reference value is 5.5. The treating of step(c) comprises treating the subject with optimal medical management (OMM)and/or palliative care (PC) when the FRP score is 5 or less, andtreating the subject with mechanical circulatory support (MCS) surgery,heart transplant (HTx) surgery, coronary artery bypass graft (CABG)surgery, percutaneous coronary interventions (PCI), aortic valvereplacement (AVR) surgery, mitral valve replacement (MVR) surgery,trans-catheter aortic valve replacement (TAVR), transcatheter mitralclip, ventricular tachycardia ablation, or stellate gangliectomy whenthe FRP score is 6 to 10. In some embodiments, the lower scores areassigned to “Group II”, while the higher scores are assigned to “GroupI”. In some embodiments, scores of 1-4 are assigned to Group II, whilescores of 7-10 are assigned to Group I, and scores of 5-6 are consideredan indeterminate zone, to be evaluated with additional circumstancestaken into account, such as factors that weigh in favor of palliativecare or factors that favor aggressive treatment, notwithstanding theless convincing FRP score.

In some embodiments, the measuring comprises polymerase chain reaction(PCR) or next generation sequencing (NGS). In some embodiments, the PCRis performed using one or more primers selected from GAPDH-f:CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ IDNO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r:ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f:AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).

In some embodiments, the measuring is performed one to three days, or 72hours, prior to treatment with an AdHF intervention. In one embodiment,the measuring is performed one day prior to treatment. In someembodiments, the subject is suffering from heart failure with reducedejection fraction or preserved ejection fraction.

Also provided is a method of predicting outcome of AdHF intervention ina patient suffering from heart failure. The method typically comprisesperforming the method of measuring gene expression in a sample asdescribed herein, wherein a poor outcome is predicted when the FRP scoreis greater than the reference value. Optionally, the method furthercomprises treating the subject with OMM, PC, MCS, HTx, or other AdHFintervention when the FRP score is less than the reference value.

Additionally provided is a method of monitoring progression of heartfailure in a subject. In one embodiment, the method comprises performingthe method of measuring gene expression in a sample as described herein.In a typical embodiment, progression is detected when the FRP score isreduced relative to a prior measurement obtained from the subject. Inone embodiment, progression is detected when the FRP score is reduced by2 (on a scale of 1-10) relative to a prior measurement.

In some embodiments, the FRP score is determined on the basis of alinear discriminant analysis (LDA) of at least 10 of the 28 genes listedin Table 3 using preoperative and postoperative expression levels of theat least 10 genes observed in a population of patients treated with AdHFintervention, wherein the FRP score is adjusted by weighting thecontribution of each of the genes in accordance with the lineardiscriminant analysis. In some embodiments, the FRP score is determinedon the basis of a linear discriminant analysis (LDA) of at least 10 ofthe genes listed in Table 4A and 4B. The linear discriminant analysiscan be based on expression levels of fewer than 10, or up to all 28 ofthe genes listed in Table 3. In some embodiments, the lineardiscriminant analysis is based on expression levels of fewer than 10, orup to all of the genes listed in Table 4A and 4B. In some embodiments,the analysis takes into account additional genes, such as those listedin Table 2, or identified elsewhere. Those skilled in the art willrecognize that the analysis can be performed using additional data froma larger patient population. In some embodiments, the preoperativeexpression levels are obtained one to three days prior to treatment. Insome embodiments, the postoperative expression levels are obtained 7-10days, and typically 5-40 days, after treatment. Examples of treatmentinclude, but are not limited to, mechanical circulatory support (MCS)surgery, heart transplant (HTx) surgery, coronary artery bypass graft(CABG) surgery, percutaneous coronary interventions (PCI), aortic valvereplacement (AVR) surgery, mitral valve replacement (MVR) surgery,trans-catheter aortic valve replacement (TAVR), transcatheter mitraldip, ventricular tachycardia ablation, or stellate gangliectomy.

Also provided is a non-transitory computer-readable medium encoded withcomputer-executable instructions for performing the methods describedherein (FIG. 2). In another embodiment, the invention provides anon-transitory computer-readable medium embodying at least one programthat, when executed by a computing device comprising at least oneprocessor, causes the computing device to perform one or more of themethods described herein. In some embodiments, the at least one programcontains algorithms, instructions or codes for causing the at least oneprocessor to perform the method(s). Likewise, the invention provides anon-transitory computer-readable storage medium storingcomputer-readable algorithms, instructions or codes that, when executedby a computing device comprising at least one processor, cause orinstruct the at least one processor to perform a method describedherein.

The invention also provides a method of treating a subject sufferingfrom heart failure. In one embodiment, the method comprises (a)measuring the expression level of a set of at least 8 genes in thesample, wherein the at least 8 genes are selected from those listed inTables 2 and Table 3; (b) assigning a Function Recovery Potential (FRP)score between 1 (lowest) and 10 (highest) to the sample that reflectsthe measured expression level of the genes in a direction associatedwith recovery from organ failure; and (c) treating the subject withoptimal medical management (OMM) and/or palliative care (PC) when theFRP score 5 or less, and referring the subject for treatment withmechanical circulatory support (MCS) surgery, heart transplant (HTx)surgery, coronary artery bypass graft (CABG) surgery, percutaneouscoronary interventions (PCI), aortic valve replacement (AVR) surgery,mitral valve replacement (MVR) surgery, trans-catheter aortic valvereplacement (TAVR), transcatheter mitral clip, ventricular tachycardiaablation, or stellate gangliectomy when the FRP score is 6 to 10. In oneembodiment, the set of genes is BATF2, AGRN, ANKRD22, DNM1P46, FRMD6,KIR2DL4, BCORP1, SAP25, NAPSA, HEXA-AS1, TIMP3, and RHBDD3.

Also described herein is a method for developing a function recoverypotential (FRP) scoring algorithm that predicts a subject's ability torecover from medical intervention for organ failure. In one embodiment,the method comprises (a) obtaining the expression levels of at least 10of the 28 genes listed in Table 3 or at least 10 of the genes listed inTable 4A and 4B using pre-intervention and post-intervention expressionlevels of the at least 10 genes observed in PBMC samples obtained from apopulation of patients treated with medical intervention for organfailure; (b) performing linear discriminant analysis of the expressionlevels obtained in (a) to classify the PBMC samples into Group I(post-intervention improvement) or Group II (non-improvement); (c)estimating the effect size of each of the gene expression levels on theclassification of a sample into Group I or Group II; and (d) adjustingthe FRP scoring algorithm by weighting the contribution of each of thegenes in accordance with the effect size. In one embodiment, the medicalintervention is surgery. In some embodiments, the surgery is an organtransplant, or provision of mechanical support for the organ, such ascirculatory support or dialysis. In some embodiments, the AdHFintervention includes treatments—but is not limited to—mechanicalcirculatory support (MCS) surgery, heart transplant (HTx) surgery,coronary artery bypass graft (CABG) surgery, percutaneous coronaryinterventions (PCI), aortic valve replacement (AVR) surgery, mitralvalve replacement (MVR) surgery, trans-catheter aortic valve replacement(TAVR), transcatheter mitral clip, ventricular tachycardia ablation, orstellate gangliectomy.

Also provided herein are methods for treating an individual, comprising:(i) receiving a sample from the individual; (ii) determining a geneexpression level in the sample for at least one gene comprising RSG1,TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431,PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4,RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1,NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1; and (iii) providing a treatment to theindividual based on the gene expression level. In some embodiments, thesample comprises blood, urine, sputum, hair, or skin. In someembodiments, the gene expression level is either an increase or adecrease in expression of the at least one gene relative to an expectedexpression level value. In some embodiments, the gene expression levelin the sample that is determined is for two genes comprising RSG1,TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431,PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4,RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1,NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1. In some embodiments, the geneexpression level is assigned a score, and wherein the treatment isdetermined based on the score. In some embodiments, the score comprisesa Function Recovery Potential (FRP) score. In some embodiments, thescore is determined based on a linear discriminant analysis of datacomprising known gene expression levels and known FRP scores of aplurality of individuals. In some embodiments, the treatment is selectedfrom—but not limited to—mechanical circulatory support (MCS) surgery,heart transplant (HTx) surgery, coronary artery bypass graft (CABG)surgery, percutaneous coronary interventions (PCI), aortic valvereplacement (AVR) surgery, mitral valve replacement (MVR) surgery,trans-catheter aortic valve replacement (TAVR), transcatheter mitralclip, ventricular tachycardia ablation, or stellate gangliectomy. Insome embodiments, the gene expression level is a level determined bypolymerase chain reaction (PCR), next generation sequencing (NGS), orother gene expression profiling assay platform such as Nanostring'sNCounter hybridization platform. In some embodiments, the PCR isperformed using at least one primer selected from GAPDH-f:CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ IDNO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r:ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f:AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).

Further provided herein are computer implemented systems, comprising:(a) a sample receiver for receiving a sample provided by an individual;(b) a digital processing device comprising an operating systemconfigured to perform executable instructions and a memory; (c) acomputer program including instructions executable by the digitalprocessing device to provide a treatment to a healthcare provider basedon the sample, the computer program comprising: (i) an gene analysismodule configured to determine a gene expression level in the sample forat least one gene comprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5,CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C,DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1,AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8,MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2,ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1,CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1,XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, orFITM1; (ii) a treatment determination module configured to determine thetreatment based on the gene expression level; and (iii) a display moduleconfigured to provide the treatment to the healthcare provider. In someembodiments, the sample comprises blood, urine, sputum, hair, or skin.In some embodiments, the gene expression level is either an increase ora decrease in expression of the at least one gene relative to anexpected expression level value. In some embodiments, the geneexpression level in the sample that is determined is for two genescomprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1,NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5,CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1,ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, or FITM1. In someembodiments, the gene expression level is assigned a score. and whereinthe treatment is determined based on the score. In some embodiments, thescore comprises a Function Recovery Potential (FRP) score. In someembodiments, the score is determined based on a linear discriminantanalysis of data comprising known gene expression levels and known FRPscores of a plurality of individuals. In some embodiments, the treatmentis selected from mechanical circulatory support (MCS) surgery, hearttransplant (HTx) surgery, coronary artery bypass graft (CABG) surgery,percutaneous coronary interventions (PCI), aortic valve replacement(AVR) surgery, mitral valve replacement (MVR) surgery, trans-catheteraortic valve replacement (TAVR), transcatheter mitral clip, ventriculartachycardia ablation, or stellate gangliectomy. In some embodiments, thegene expression level is a level determined by polymerase chain reaction(PCR), next generation sequencing (NGS), or other gene expressionprofiling assay platform such as Nanostring's NCounter hybridizationplatform. In some embodiments, the PCR is performed using at least oneprimer selected from GAPDH-f: CCACTCCTCCACCTTTGAC (SEQ ID NO: 1);GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ ID NO: 2); KIR2DL4-f:ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r: ATCACAGCATGCAGGTGTCT(SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQ ID NO: 5); NAPSA-r:GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f: AAAGGCAGCTGAAGAAGCAG (SEQID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQ ID NO: 8); ANKRD22-f:CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r: TGATAGGCTGCTTGGCAGAT(SEQ ID NO: 10).

Described herein is a Non-transitory computer-readable storage mediaencoded with a computer program including instructions executable by aprocessor to cause the processor to determine a gene expression level ina sample for at least one gene comprising RSG1, TPRA1, SAP25, MFSD3,FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6,TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63,BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1,NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164,C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2,C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1,SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1; and provide a suggestion for atreatment to the individual based on the gene expression level. In someembodiments, the gene expression level is either an increase or adecrease in expression of the at least one gene relative to an expectedexpression level value. In some embodiments, the gene expression levelin the sample that is determined is for two genes comprising RSG1,TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431,PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4,RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1,NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1. In some embodiments, the geneexpression level is assigned a score, and wherein the treatment isdetermined based on the score. In some embodiments. the score comprisesa Function Recovery Potential (FRP) score. In some embodiments, thescore is determined based on a linear discriminant analysis of datacomprising known gene expression levels and known FRP scores of aplurality of individuals. In some embodiments, the treatment is selectedfrom mechanical circulatory support (MCS) surgery, heart transplant(HTx) surgery, coronary artery bypass graft (CABG) surgery, percutaneouscoronary interventions (PCI), aortic valve replacement (AVR) surgery,mitral valve replacement (MVR) surgery, trans-catheter aortic valvereplacement (TAVR), transcatheter mitral clip, ventricular tachycardiaablation, or stellate gangliectomy. In some embodiments, the geneexpression level is a level determined by polymerase chain reaction(PCR), next generation sequencing (NGS) or other platform such asNanostring's NCounter hybridization platform. In some embodiments, thePCR is performed using at least one primer selected from GAPDH-f:CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ IDNO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r:ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f:AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1A-1B show schematic illustrations of exemplary methods andframeworks described herein. FIG. 1A shows a schematic of an exemplarypredictive model and theoretical framework. FIG. 1B shows an exemplaryalgorithm for determining an FRP score from gene expression values ofselected genes.

FIG. 2 is a block diagram of an embodiment of a computer system that canbe used to implement a method as described herein.

FIGS. 3A-3B illustrate organ function and outcomes. FIG. 3A shows organfunction and outcomes of 29 patients across five time points. FIG. 3Bshows that, out of 29 AdHF-patients undergoing MCS-surgery, 17 patientsimproved (Group I, upper right quadrant) and 12 patients did not improve(Group II, remaining 3 quadrants) from day −1 (TP1) to day 8 (TP5). Eachlarge dark bullet represents one patient who died within one year.

FIG. 4 shows the Kaplan-Meier 1-year survival in Group I vs. Group II.

FIGS. 5A-5C show overlap of significant genes associated with organfunction improvement and survival benefit. The indicated color range inFIGS. 5A and 5B corresponds to the differential expression, ranging fromblue (−2) to gray, to yellow (0), to orange, to red (1.1). FIG. 5A showshierarchical clustering of significant genes day −1 (TP1). Left: TheVolcano plot of 28 genes, which are differentially expressed betweenGroup I and Group II. Right: Hierarchical clustering of the 28 candidategenes for the prediction test demonstrates the differential geneexpression between Group I and Group II. FIG. 5B shows hierarchicalclustering of genes associated with survival benefit. Left: The Volcanoplot of 105 genes, which are differentially expressed between Group Iand Group II. Right: Hierarchical clustering 17 of the 105 candidategenes for the prediction test demonstrates the differential geneexpression between Group I=Survival, Group II=Non-survival. FIG. 5Cshows overlap genes from both improvement group and 1-year survivaloutcome. Left: Venn-Diagram shows the 28 DEGs identified in thecomparison by Improvement Score (red; left circle) and the Right showsthe 105 DEGs identified by comparing 1-Year Survival (blue; rightcircle). 12 DEGs were shared across the two comparisons. Right: The 12overlap genes.

FIG. 6 shows an exemplary prediction biomarker development rationale.

FIG. 7 illustrates the concept of FRP, a clinical composite parameterthat can include chronological age as well as personal biological age(measurable by established and validated HF-, OD-, comorbidity-,frailty- and disability-instruments), that represents the instantaneouspotential of the person with AdHF to cope with and survive stressorssuch as AdHF-surgical/interventional therapies, and can be diagnosed bya molecular biomarker.

FIG. 8 shows two case studies out of the 29 AdHF-patients in theProof-Of-Concept Study that illustrate the clinical utility of FRPscoring. The indicated color range corresponds to the differentialexpression, ranging from blue (−2) to gray, to yellow (0), to orange, tored (1.1).

DETAILED DESCRIPTION OF THE INVENTION

Described herein are methods and systems for providing a treatment to anindividual based on a gene expression profile assay and classificationsystem for predicting whether an individual has the potential to recoverorgan function after AdHF-surgical/interventional therapies,particularly for an individual suffering from heart failure and/ormultiorgan dysfunction syndrome.

Definitions

All scientific and technical terms used in this application havemeanings commonly used in the art unless otherwise specified. As used inthis application, the following words or phrases have the meaningsspecified.

As used herein, “AdHF intervention” refers to treatments for advancedheart failure. Representative examples of AdHF surgical/interventionaltherapies include, but are not limited to: mechanical circulatorysupport (MCS), heart transplantation (HTx), coronary artery bypass graft(CABG) surgery, percutaneous coronary interventions (PCI), aortic valvereplacement (AVR) surgery, mitral valve replacement (MVR) surgery,trans-catheter aortic valve replacement (TAVR), transcatheterMitra-Clip, ventricular tachycardia ablation and stellate gangliectomy.

As used herein, “reference” in the context of gene expression levelsrefers to that observed in healthy volunteers, or in a subject whorecovers from heart failure and/or major organ dysfunction. In someembodiments, the reference group is a set of normalization or controlgenes, as described herein.

As used herein, a “normalization gene” refers to a gene whose measuredexpression level does not discriminate between subjects who improve andthose who do not improve with small standard deviations across samples.

As used herein, “a” or “an” means at least one, unless clearly indicatedotherwise.

As used herein, to “prevent” or “protect against” a condition or diseasemeans to hinder, reduce or delay the onset or progression of thecondition or disease.

“About” a number, as used herein, refers to range including the numberand ranging from 10% below that number to 10% above that number. “About”a range refers to 10% below the lower limit of the range, spanning to10% above the upper limit of the range.

As used herein, the terms “treatment,” “treating,” “ameliorating asymptom,” and the like, in some cases, refer to administering an agent,or carrying out a procedure, for the purposes of obtaining an effect.The effect may be prophylactic in terms of completely or partiallypreventing a disease or symptom thereof and/or may be therapeutic interms of effecting a partial or complete cure for a disease and/orsymptoms of the disease. “Treatment,” as used herein, may includetreatment of a heart condition, such as heart failure, in a mammal,particularly in a human, and includes: (a) preventing the disease or asymptom of a disease from occurring in a subject which may bepredisposed to the disease but has not yet been diagnosed as having it(e.g., including diseases that may be associated with or caused by aprimary disease; (b) inhibiting the disease, i.e., arresting itsdevelopment; and (c) relieving the disease, i.e., causing regression ofthe disease. Treating may refer to any indicia of success in thetreatment or amelioration or prevention of an heart condition, includingany objective or subjective parameter such as abatement; remission;diminishing of symptoms or making the disease condition more tolerableto the patient; slowing in the rate of degeneration or decline; ormaking the final point of degeneration less debilitating. The treatmentor amelioration of symptoms is based on one or more objective orsubjective parameters; including the results of an examination by aphysician. Accordingly, the term “treating” includes the administrationof the compounds or agents of the present invention to prevent or delay,to alleviate, or to arrest or inhibit development of the symptoms orconditions associated with heart disease or other diseases. The term“therapeutic effect” refers to the reduction, elimination, or preventionof the disease, symptoms of the disease, or side effects of the diseasein the subject. A treatment, in some embodiments, comprises taking acourse of action with respect to an individual. In some embodiments, acourse of action taken with respect to an individual comprises nomedical procedure being performed on the individual. In, someembodiments, a course of action taken with respect to an individualcomprises conservative care or no additional care being provided.

Methods of Treatment

In some embodiments, there are provided methods of treating anindividual in need thereof, such as methods of treating an individualsuffering from a heart disease, such as heart failure. In someembodiments, there are provided methods of predicting treatment outcomesin an individual in need of heart failure interventional/surgicaltherapies. Some such methods comprise obtaining a gene expression valuefrom a biological sample from the individual. In some embodiments,methods of treatment herein comprise (i) receiving a sample from anindividual; and (ii) determining a gene expression level in the samplefor at least one gene. In some embodiments, methods of treatment hereincomprise providing a treatment to the individual based on the geneexpression level. In some embodiments, methods herein compriserecommending a treatment to the individual based on the gene expressionlevel. In some embodiments, the gene comprises at least one of RSG1,TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431,PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4,RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1,NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1. In some embodiments, the geneexpression level in the sample is determined for at least two genes ofRSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA,NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22,BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1,LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF,RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3,SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2,AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY,TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, or FITM1.

In some embodiments, methods of treatment provided herein furthercomprise determining a function recovery potential (FRP) score based onthe gene expression level. Function Recovery Potential (FRP=Resilience)score between 1 (lowest) and 10 (highest) to the sample that reflectsthe measured expression level of the genes. In one embodiment, asexemplified in the proof-of-concept study (Example 1), the FRP isdefined using the Sequential Organ Failure Assessment score and Model ofEnd-stage Liver Disease Except INR score (measured one day before andeight days after surgery): Group I=improving (both scores improved fromday −1 to day 8) and Group II=not improving (either one or both scoresdid not improve from day −1 to day 8). The FRP correlates with 1-yearsurvival. In some embodiments, the method further comprises (c)selecting a treatment of optimal medical management (OMM) and/orpalliative care (PC) for the individual when the FRP score is 5 or less,and selecting a treatment of an AdHF intervention for the individualwhen the FRP score is 6 to 10. Examples of treatments comprising AdHFintervention include, but are not limited to, mechanical circulatorysupport (MCS) surgery, heart transplant (HTx) surgery, coronary arterybypass graft (CABG) surgery, percutaneous coronary interventions (PCI),aortic valve replacement (AVR) surgery, mitral valve replacement (MVR)surgery, trans-catheter aortic valve replacement (TAVR), transcathetermitral clip, ventricular tachycardia ablation, and stellategangliectomy.

FIG. 1A illustrates an exemplary predictive model and theoreticalframework. Worsening heart failure with reduced ejection fraction(HFrEF; left panel) is linked to progressive frailty/organ dysfunction(OD) via neuro-endocrine-immune activation mediated by complexinteractions between diseased myocyte, peripheral blood mononuclearcells (PBMCs), endothelial cells (EC), and platelets (PLT) (middlepanel). Outcome and comparative survival benefit prediction is improvedby adding the molecular immunology biomarker or multidimensionalmolecular biomarker (MMB) to clinical predictors (upward arrow in bottomright ROC curve; right panel). FIG. 1B shows an exemplary algorithm fordetermining an FRP score from gene expression values of selected genesas described herein.

Gene Expression Profiling

In one embodiment, the invention provides a method of measuring geneexpression in a biological sample obtained from a subject suffering fromheart failure. In a related embodiment, the invention provides a methodof reducing risk and optimizing treatment outcome for a subjectsuffering from organ failure. In one embodiment, the method comprisesprofiling gene expression in a sample of peripheral blood mononuclearcells (PBMCs). Typical steps of the method comprise: (a) measuring theexpression level of a set of genes in the sample, wherein the set ofgenes are selected from those listed Tables 1A-1I, Table 2, Table 3, orTable 4A and 4B; and, based on this gene expression test result, (b)assigning a—clinical—Function Recovery Potential (FRP=Resilience) scorebetween 1 (lowest) and 10 (highest) to the sample that reflects themeasured expression level of the genes. The FRP is defined using theSequential Organ Failure Assessment score and Model of End-stage LiverDisease Except INR score (measured one day before and eight days aftersurgery): Group I=improving (both scores improved from day −1 to day 8)and Group II=not improving (either one or both scores did not improvefrom day −1 to day 8). The FRP correlates with 1-year survival. In someembodiments, therefore, the method further comprises (c) referring thesubject for treatment with optimal medical management (OMM) and/orpalliative care (PC) if the FRP score is 5 or less, and referring thesubject for treatment with an AdHF intervention if the FRP score is 6 to10. Examples of AdHF intervention include, but are not limited to,mechanical circulatory support (MCS) surgery, heart transplant (HTx)surgery, coronary artery bypass graft (CABG) surgery, percutaneouscoronary interventions (PCI), aortic valve replacement (AVR) surgery,mitral valve replacement (MVR) surgery, trans-catheter aortic valvereplacement (TAVR), transcatheter mitral clip, ventricular tachycardiaablation, and stellate gangliectomy.

In some embodiments, the gene expression level varies from an expectedexpression level value. In some embodiments, the gene expression levelis determined relative to another gene, such as a housekeeping gene orother appropriate gene exemplified herein. In some embodiments, the geneexpression level is increased relative to an expected expression levelvalue. In some embodiments, the gene expression level is decreasedrelative to an expected expression level value. In some cases, the geneexpression level is assigned a score, such as an FRP score. In someembodiments, methods of treatment herein are determined based on thescore. In some embodiments, the score is determined based on a lineardiscriminant analysis of data comprising known gene expression levelsand known FRP scores of a plurality of individuals.

The number of genes included in the set of genes whose expression levelis measured can range, during iterations of test development, from 5 to100. In a typical embodiment, the expression level of at least 8 genesis measured. In some embodiments, the expression level of 10-75 genes ismeasured. In other embodiments, the expression level of 10-20, or 10-30genes is measured. In one embodiment, the expression level of 10-15genes is measured. In some illustrative specific embodiments, the set ofgenes is at least 10 of the genes listed in Table 2, at least 10 of thegenes listed in Table 3 or at least 10 of the genes listed in Table 4Aor 48, or comprises one gene selected from each of Tables 1A-1I. In someembodiments, all of the genes listed in Tables 1, 2, 3, and/or 4 aremeasured. In one embodiment, the expression level of at least one genein each of Tables 1A-1I is measured.

In a typical embodiment, the measuring comprises, for example, any oneor a combination of RNA quantification, such as by next generationsequencing (NGS), polymerase chain reaction (PCR), gene arraytechnology, or a hybridization platform (such as, for example, theNanoString nCounter system). In one embodiment, the measuring employsNanoString NCounter Hybridization. Those skilled in the art willappreciate alternative methods of measuring gene expression that can beemployed. Likewise, where amplification methods require the use ofprimers, those skilled in the art can obtain appropriate primers eitherby referring to the exemplary primers described herein, or throughpublicly accessible databases. The methods can be performed using, forexample, techniques for detecting gene expression, such as PCR,including RT-PCR, RNA-Seq, DNA microarrays, etc. Other assays can beemployed, as will be understood to those skilled in the art.

For use in the methods described herein, representative examples of thesample include, but are not limited to, blood, plasma, serum, urine, orsputum, and other bodily fluids, particularly those containing PBMCs.PBMCs can be isolated, for example, from venous blood obtained viaphlebotomy. RNA can be isolated from the PBMCs for use in the assaysdescribed herein. In some embodiments, the sample comprises blood,urine, sputum, hair, or skin.

In one embodiment, the method further comprises measuring normalizationgenes that will be empirically selected using PCR data from the trainingsamples. Genes that do not discriminate between subjects who improve andthose who do not improve with small standard deviations across allsamples are considered as normalization genes. Specifically,normalization genes are chosen from a set of over 200 gene assays thatinclude at least 10 genes described in the literature as housekeepinggenes. The final control genes are selected using the followingcriteria: First, the amplicon assays need to show very low variance.Second, they must not show discrimination between Group I and Group IIsamples. Third, we identify amplicons that cover a range of CT values ina typical CP tube sample so as to control for CT dependent efficiencychanges. The use of a set of control genes to normalize the amount ofRNA present is based on the premise that an average from severalmeasurements would be more robust than any single measurement and wouldalso take into account any RNA abundance dependent effects.

In a typical embodiment, the measuring is performed one day prior to MCSor HTx surgery or other AdHF intervention. In some embodiments, themeasuring is performed within 72 hours prior to surgery or otherintervention. This approach facilitates tailoring the treatment of thesubject to the subject's condition and prospects for improvement andrecovery at the relevant point in time. A subject whose FRP score leadsto recommending treatment with OMM or PC at one point in time can beevaluated again at a later point in time and subsequently be recommendedfor treatment with MCS or HTx or other AdHF intervention.

In some embodiments, the subject is suffering from heart failure withreduced ejection fraction. In some embodiments, the subject is sufferingfrom heart failure with preserved ejection fraction.

The invention provides a method of predicting outcome of MCS or HTx orother AdHF intervention in a patient suffering from heart failure,comprising performing the method described above, wherein a poor outcomeis predicted if the FRP score is 5 or less. In some embodiments, themethods of the invention further comprise treating the subject with OMM,PC, MCS, or HTx or other AdHF intervention, in accordance with the FRPscore. In some embodiments, a poor outcome is predicted if the FRP scoreis 1-4, a score of 7-10 is predictive of recovery following advancedheart failure intervention, and a score of 5-6 is consideredintermediate. For intermediate cases, other factors may be brought intothe decision-making for treatment. In some embodiments, such otherfactors will include a subject's willingness to accept greater risk, ora preference for less aggressive treatment.

Further provided herein are methods of treating an individual. In someembodiments, methods of treatment comprise treatment for heart disease,such as heart failure or congestive heart failure. Methods of treatmentprovided herein comprise (i) receiving a sample from an individual; (ii)determining a gene expression level in the sample for at least one gene;and providing a treatment to the individual based on the gene expressionlevel. In some embodiments, the gene comprises at least one of RSG1,TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431,PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4,RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1,NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY,TXLNG2P, KDM5D, EIF1AY, or FITM1. In some embodiments, the geneexpression level in the sample is determined for at least two genes ofRSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA,NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22,BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1,LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF,RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3,SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2,AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY,TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, or FITM1.

Also provided is a method of monitoring progression of heart failure ina subject. In one embodiment, the method comprises performing the methoddescribed above, wherein progression is detected if the FRP score isreduced by 2 relative to a prior measurement and wherein improvement isdetected if the FRP score is increased by 2 relative to a priormeasurement.

In some embodiments, the FRP corresponds to the measured expressionlevel of the set of genes relative to a reference group of expressionlevels. The reference group may correspond to expression levels of theset of genes observed in healthy volunteers, or in a subject whorecovers from heart failure and/or major organ dysfunction. In someembodiments, the reference group is a set of normalization or controlgenes, as described above.

Provided below is a list of genes whose expression levels can bemeasured for this assay. The 28 predictive genes have been grouped byWGCNA-derived modules representing integrated systems biological roles.Tables 1A-1I (or referred to collectively as “Table 1”) list the 28genes identified using a Mann-Whitney test based evaluation of datapredicting day 8 organ function recovery. The full list of 28 genesappears as one group in Table 3 (known gene function summary in Table5). Table 2 lists the 71 genes whose expression is predictive of day 8organ function recovery based on t-test evaluation of data. Table 4Alists 12 genes that overlap between the genes listed in Table 3,predictive of day 8 organ function recovery, and genes whose expressionis predictive of one-year survival. Table 4B lists genes whoseexpression is predictive of one-year survival.

Gene Test Combination Options:

In one embodiment, the gene is at least one gene selected from Table1-4. In one embodiment, at least two or more genes of Table 1, 2 and/or3 is used in combination. In one embodiment, at least three or moregenes of Table 1, 2 and/or 3 is used in combination. In one embodiment,at least four or more genes of Table 1, 2 and/or 3 is used incombination. In one embodiment, at least five or more genes of Table 1,2 and/or 3 is used in combination. In one embodiment, at least six ormore genes of Table 1, 2 and/or 3 is used in combination. In oneembodiment, at least seven or more genes of Table 1, 2 and/or 3 is usedin combination. In one embodiment, at least eight or more genes of Table1, 2 and/or 3 is used in combination. In one embodiment, at least nineor more genes of Table 1, 2 and/or 3 is used in combination. In oneembodiment, at least ten or more genes of Table 1, 2 and/or 3 is used incombination. In one embodiment, at least 11 or more genes of Table 1, 2and/or 3 is used in combination. In one embodiment, at least 12 or moregenes of Table 1, 2 and/or 3 is used in combination. In one embodiment,at least 13 or more genes of Table 1, 2 and/or 3 is used in combination.In one embodiment, at least 14 or more genes of Table 1, 2 and/or 3 isused in combination. In one embodiment, at least 15 or more genes ofTable 1, 2 and/or 3 is used in combination. In one embodiment, at least16 or more genes of Table 1, 2 and/or 3 is used in combination. In oneembodiment, at least 17 or more genes of Table 1, 2 and/or 3 is used incombination. In one embodiment, at least 18 or more genes of Table 1, 2and/or 3 is used in combination. In one embodiment, at least 19 or moregenes of Table 1, 2 and/or 3 is used in combination. In one embodiment,at least 20 or more genes of Table 1, 2 and/or 3 is used in combination.In one embodiment, at least 21 or more genes of Table 1, 2 and/or 3 isused in combination. In one embodiment, at least 22 or more genes ofTable 1, 2 and/or 3 is used in combination. In one embodiment, at least23 or more genes of Table 1, 2 and/or 3 is used in combination. In oneembodiment, at least 24 or more genes of Table 1, 2 and/or 3 is used incombination. In one embodiment, at least 25 or more genes of Table 1, 2and/or 3 is used in combination. In one embodiment, at least 26 or moregenes of Table 1, 2 and/or 3 is used in combination. In one embodiment,at least 27 or more genes of Table 1, 2 and/or 3 is used in combination.In one embodiment, at least 28 or more genes of Table 1, 2 and/or 3 isused in combination. Examples of combinations of genes include: BATF2and an additional gene selected from Table 1, 2, or 3; AGRN and anadditional gene selected from Table 1, 2, or 3; ANKRD22 and anadditional gene selected from Table 1, 2, or 3; DNM1P46 and anadditional gene selected from Table 1, 2, or 3; FRMD6 and an additionalgene selected from Table 1, 2, or 3; IL-17A and an additional geneselected from Table 1, 2, or 3; KIR2DL4 and an additional gene selectedfrom Table 1, 2, or 3; BCORP1 and an additional gene selected from Table1, 2, or 3; SAP25 and an additional gene selected from Table 1, 2, or 3;NAPSA and an additional gene selected from Table 1, 2, or 3; HEXA-AS1and an additional gene selected from Table 1, 2, or 3; TIMP3 and anadditional gene selected from Table 1, 2, or 3; RHBDD3 and an additionalgene selected from Table 1, 2, or 3; any combination of 3 or more genesselected from Tables 1, 2 and 3; any combination of 4, 5, 6, 7, 8, 9,10, 11, or 12 genes of Tables 1, 2 and 3. In one embodiment, thecombination of genes is 2, 3, 4, or 5 genes selected from Table 1. Inanother embodiment, the combination of genes is at least one gene fromTable 1, and at least one gene from Table 2. In another embodiment, thecombination of genes is at least one gene from Table 1, and at least onegene from Table 3. In another embodiment, the combination of genes is atleast one gene from Table 2, and at least one gene from Table 3. Inanother embodiment, the combination of genes is at least one gene fromeach of Tables 1, 2, and 3. In one embodiment, the combination of genesis all or a subset of the genes listed in one or more of Tables 1-4.

In some embodiments, the combination of genes comprises at least onegene selected from Table 1A. In some embodiments, the combination ofgenes comprises at least one gene selected from Table 1B. In someembodiments, the combination of genes comprises at least one geneselected from Table 1C. In some embodiments, the combination of genescomprises at least one gene selected from Table 1D. In some embodiments,the combination of genes comprises at least one gene selected from Table1E. In some embodiments, the combination of genes comprises at least onegene selected from Table 1F. In some embodiments, the combination ofgenes comprises at least one gene selected from Table 1G. In someembodiments, the combination of genes comprises at least one geneselected from Table 1H. In some embodiments, the combination of genescomprises at least one gene selected from Table 1I. In some embodiments,the combination of genes comprises at least two genes selected fromTable 1A. In some embodiments, the combination of genes comprises atleast two genes selected from Table 1B. In some embodiments, thecombination of genes comprises at least two genes selected from Table1C. In some embodiments, the combination of genes comprises at least twogenes selected from Table 1D. In some embodiments, the combination ofgenes comprises at least two genes selected from Table 1E. In someembodiments, the combination of genes comprises at least two genesselected from Table 1F. In some embodiments, the combination of genescomprises at least two genes selected from Table 1G. In someembodiments, the combination of genes comprises at least two genesselected from Table 1H. In some embodiments, the combination of genescomprises at least one gene selected from Table 1A, at least one geneselected from Table 1B, at least one gene selected from Table 1C, atleast one gene selected from Table 1D, at least one gene selected fromTable 1E, at least one gene selected from Table 1F, at least one geneselected from Table 1G, at least one gene selected from Table 1H, and atleast one gene selected from Table 1I. In some embodiments, thecombination of genes comprises at least one gene selected from Table 1A,at least one gene selected from Table 1B, at least one gene selectedfrom Table 1C, at least one gene selected from Table 1D, at least onegene selected from Table 1E, at least one gene selected from Table 1F,at least one gene selected from Table 1G, at least one gene selectedfrom Table 1H, and/or at least one gene selected from Table 1I.

In some embodiments, the combination of genes comprises at least onegene selected from Table 2. In some embodiments, the combination ofgenes comprises at least two genes selected from Table 2. In someembodiments, the combination of genes comprises at least three genesselected from Table 2. In some embodiments, the combination of genescomprises at least four genes selected from Table 2. In someembodiments, the combination of genes comprises at least five genesselected from Table 2. In some embodiments, the combination of genescomprises at least six genes selected from Table 2. In some embodiments,the combination of genes comprises at least seven genes selected fromTable 2. In some embodiments, the combination of genes comprises atleast eight genes selected from Table 2. In some embodiments, thecombination of genes comprises at least nine genes selected from Table2. In some embodiments, the combination of genes comprises at least tengenes selected from Table 2. In some embodiments, the combination ofgenes comprises at least 11 genes selected from Table 2. In someembodiments, the combination of genes comprises at least 12 genesselected from Table 2. In some embodiments, the combination of genescomprises at least 13 genes selected from Table 2. In some embodiments,the combination of genes comprises at least 14 genes selected from Table2. In some embodiments, the combination of genes comprises at least 15genes selected from Table 2. In some embodiments, the combination ofgenes comprises at least 16 genes selected from Table 2. In someembodiments, the combination of genes comprises at least 17 genesselected from Table 2. In some embodiments, the combination of genescomprises at least 18 genes selected from Table 2. In some embodiments,the combination of genes comprises at least 19 genes selected from Table2. In some embodiments, the combination of genes comprises at least 20genes selected from Table 2. In some embodiments, the combination ofgenes comprises at least 21 genes selected from Table 2. In someembodiments, the combination of genes comprises at least 22 genesselected from Table 2. In some embodiments, the combination of genescomprises at least 23 genes selected from Table 2. In some embodiments,the combination of genes comprises at least 24 genes selected from Table2. In some embodiments, the combination of genes comprises at least 25genes selected from Table 2. In some embodiments, the combination ofgenes comprises at least 26 genes selected from Table 2. In someembodiments, the combination of genes comprises at least 27 genesselected from Table 2. In some embodiments, the combination of genescomprises at least 28 genes selected from Table 2. In some embodiments,the combination of genes comprises AGRN and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesRSG1 and at least one additional gene from Table 2. In some embodiments,the combination of genes comprises LOC728431 and at least one additionalgene from Table 2. In some embodiments, the combination of genescomprises PDZK1IP1 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises NEGR1 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises HMCN1 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises CKAP2L and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises ACVR1C and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesKCNH8 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises CCR8 and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises TPRA1 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises IGSF10 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises MME and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises ETV5 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises CXCL9 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises HBEGF and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises RANBP17 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesDDX43 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises C6orf164 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises GPR63 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises SLC22A1 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises C7orf50 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesSAP25 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises NEFL and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises CDCA2 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises MFSD3 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises ALDH1A1 and at least one additional gene from Table2. In some embodiments, the combination of genes comprises OLFM1 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises ANKRD22 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesFADS3 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises BATF2 and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises SAC3D1 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises FZD4 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises FITM1 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises FRMD6 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises SPTBN5 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesRBPMS2 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises HEXA-AS1 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises C15orf38 and at least one additional gene from Table2. In some embodiments, the combination of genes comprises DNM1P46 andat least one additional gene from Table 2. In some embodiments, thecombination of genes comprises CEMP1 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesST6GALNAC1 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises CHMP6 and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises ASPSCR1 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises SKA1 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises CD209 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesSNAPC2 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises AXL and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises NAPSB and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises NAPSA and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises KIR2DL1 and at least one additional gene from Table2. In some embodiments, the combination of genes comprises KIR2DL4 andat least one additional gene from Table 2. In some embodiments, thecombination of genes comprises NLRP2 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesNTSR1 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises SEPT5 and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises RHBDD3 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises TIMP3 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises KAL1 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises PRRG1 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises XIST and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesRPS4Y1 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises ZFY and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises PRKY and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises TTTY15 and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises USP9Y and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises DDX3Y and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises UTY and at least one additional gene fromTable 2. In some embodiments, the combination of genes comprises BCORP1and at least one additional gene from Table 2. In some embodiments, thecombination of genes comprises TXLNG2P and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesKDM5D and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises EIF1AY and at least oneadditional gene from Table 2.

In some embodiments, the combination of genes comprises at least onegene selected from Table 3. In some embodiments, the combination ofgenes comprises at least two genes selected from Table 3. In someembodiments, the combination of genes comprises at least three genesselected from Table 3. In some embodiments, the combination of genescomprises at least four genes selected from Table 3. In someembodiments, the combination of genes comprises at least five genesselected from Table 3. In some embodiments, the combination of genescomprises at least six genes selected from Table 3. In some embodiments,the combination of genes comprises at least seven genes selected fromTable 3. In some embodiments, the combination of genes comprises atleast eight genes selected from Table 3. In some embodiments, thecombination of genes comprises at least nine genes selected from Table3. In some embodiments, the combination of genes comprises at least tengenes selected from Table 3. In some embodiments, the combination ofgenes comprises at least 11 genes selected from Table 3. In someembodiments, the combination of genes comprises at least 12 genesselected from Table 3. In some embodiments, the combination of genescomprises at least 13 genes selected from Table 3. In some embodiments,the combination of genes comprises at least 14 genes selected from Table3. In some embodiments, the combination of genes comprises at least 15genes selected from Table 3. In some embodiments, the combination ofgenes comprises at least 16 genes selected from Table 3. In someembodiments, the combination of genes comprises at least 17 genesselected from Table 3. In some embodiments, the combination of genescomprises at least 18 genes selected from Table 3. In some embodiments,the combination of genes comprises at least 19 genes selected from Table3. In some embodiments, the combination of genes comprises at least 20genes selected from Table 3. In some embodiments, the combination ofgenes comprises at least 21 genes selected from Table 3. In someembodiments, the combination of genes comprises at least 22 genesselected from Table 3. In some embodiments, the combination of genescomprises at least 23 genes selected from Table 3. In some embodiments,the combination of genes comprises at least 24 genes selected from Table3. In some embodiments, the combination of genes comprises at least 25genes selected from Table 3. In some embodiments, the combination ofgenes comprises at least 26 genes selected from Table 3. In someembodiments, the combination of genes comprises at least 27 genesselected from Table 3. In some embodiments, the combination of genescomprises USP9Y and at least one additional gene from Table 3. In someembodiments, the combination of genes comprises BATF2 and at least oneadditional gene from Table 3. In some embodiments, the combination ofgenes comprises AGRN and at least one additional gene from Table 3. Insome embodiments, the combination of genes comprises ANKRD22 and atleast one additional gene from Table 3. In some embodiments, thecombination of genes comprises HMCN1 and at least one additional genefrom Table 3. In some embodiments, the combination of genes comprisesACVR1C and at least one additional gene from Table 3. In someembodiments, the combination of genes comprises GPR63 and at least oneadditional gene from Table 3. In some embodiments, the combination ofgenes comprises DNM1P46 and at least one additional gene from Table 3.In some embodiments, the combination of genes comprises CKAP2L and atleast one additional gene from Table 3. In some embodiments, thecombination of genes comprises FRMD6 and at least one additional genefrom Table 3. In some embodiments, the combination of genes comprisesKIR2DL4 and at least one additional gene from Table 3. In someembodiments, the combination of genes comprises IGSF10 and at least oneadditional gene from Table 3. In some embodiments, the combination ofgenes comprises BCORP1 and at least one additional gene from Table 3. Insome embodiments, the combination of genes comprises SAP25 and at leastone additional gene from Table 3. In some embodiments, the combinationof genes comprises NAPSA and at least one additional gene from Table 3.In some embodiments, the combination of genes comprises FITM1 and atleast one additional gene from Table 3. In some embodiments, thecombination of genes comprises SPTBN5 and at least one additional genefrom Table 3. In some embodiments, the combination of genes comprisesHEXA-AS1 and at least one additional gene from Table 3. In someembodiments, the combination of genes comprises SLC22A1 and at least oneadditional gene from Table 3. In some embodiments, the combination ofgenes comprises RSG1 and at least one additional gene from Table 3. Insome embodiments, the combination of genes comprises TIMP3 and at leastone additional gene from Table 3. In some embodiments, the combinationof genes comprises TPRA1 and at least one additional gene from Table 3.In some embodiments, the combination of genes comprises CEMP1 and atleast one additional gene from Table 3. In some embodiments, thecombination of genes comprises ASPSCR1 and at least one additional genefrom Table 3. In some embodiments, the combination of genes comprisesMFSD3 and at least one additional gene from Table 3. In someembodiments, the combination of genes comprises NAPSB and at least oneadditional gene from Table 3. In some embodiments, the combination ofgenes comprises NLRP2 and at least one additional gene from Table 3. Insome embodiments, the combination of genes comprises RHBDD3 and at leastone additional gene from Table 3.

In some embodiments, the combination of genes comprises at least onegene selected from Table 4A. In some embodiments, the combination ofgenes comprises at least two genes selected from Table 4A. In someembodiments, the combination of genes comprises at least three genesselected from Table 4A. In some embodiments, the combination of genescomprises at least four genes selected from Table 4A. In someembodiments, the combination of genes comprises at least five genesselected from Table 4A. In some embodiments, the combination of genescomprises at least six genes selected from Table 4A. In someembodiments, the combination of genes comprises at least seven genesselected from Table 4A. In some embodiments, the combination of genescomprises at least eight genes selected from Table 4A. In someembodiments, the combination of genes comprises at least nine genesselected from Table 4A. In some embodiments, the combination of genescomprises at least ten genes selected from Table 4A. In someembodiments, the combination of genes comprises at least 11 genesselected from Table 4A. In some embodiments, the combination of genescomprises BATF2 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises AGRN and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises ANKRD22 and at least one additional gene from Table 2.In some embodiments, the combination of genes comprises DNM1P46 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises FRMD6 and at least one additional genefrom Table 2. In some embodiments, the combination of genes comprisesKIR2DL4 and at least one additional gene from Table 2. In someembodiments, the combination of genes comprises BCORP1 and at least oneadditional gene from Table 2. In some embodiments, the combination ofgenes comprises SAP25 and at least one additional gene from Table 2. Insome embodiments, the combination of genes comprises NAPSA and at leastone additional gene from Table 2. In some embodiments, the combinationof genes comprises HEXA-AS1 and at least one additional gene from Table2. In some embodiments, the combination of genes comprises TIMP3 and atleast one additional gene from Table 2. In some embodiments, thecombination of genes comprises RHBDD3 and at least one additional genefrom Table 2.

In some embodiments, the combination of genes comprises at least onegene selected from Table 4B. In some embodiments, the combination ofgenes comprises at least two genes selected from Table 4B. In someembodiments, the combination of genes comprises at least three genesselected from Table 4B. In some embodiments, the combination of genescomprises at least four genes selected from Table 4B. In someembodiments, the combination of genes comprises at least five genesselected from Table 4B. In some embodiments, the combination of genescomprises at least six genes selected from Table 4B. In someembodiments, the combination of genes comprises at least seven genesselected from Table 4B. In some embodiments, the combination of genescomprises at least eight genes selected from Table 4B. In someembodiments, the combination of genes comprises at least nine genesselected from Table 4B. In some embodiments, the combination of genescomprises at least ten genes selected from Table 4B. In someembodiments, the combination of genes comprises at least 11 genesselected from Table 4B. In some embodiments, the combination of genescomprises at least 12 genes selected from Table 4B. In some embodiments,the combination of genes comprises at least 13 genes selected from Table4B. In some embodiments, the combination of genes comprises at least 14genes selected from Table 4B. In some embodiments, the combination ofgenes comprises at least 15 genes selected from Table 4B. In someembodiments, the combination of genes comprises at least 16 genesselected from Table 4B. In some embodiments, the combination of genescomprises at least 17 genes selected from Table 4B. In some embodiments,the combination of genes comprises at least 18 genes selected from Table4B. In some embodiments, the combination of genes comprises at least 19genes selected from Table 4B. In some embodiments, the combination ofgenes comprises at least 20 genes selected from Table 4B. In someembodiments, the combination of genes comprises at least 21 genesselected from Table 4B. In some embodiments, the combination of genescomprises at least 22 genes selected from Table 4B. In some embodiments,the combination of genes comprises at least 23 genes selected from Table4B. In some embodiments, the combination of genes comprises at least 24genes selected from Table 4B. In some embodiments, the combination ofgenes comprises at least 25 genes selected from Table 4B. In someembodiments, the combination of genes comprises at least 26 genesselected from Table 4B. In some embodiments, the combination of genescomprises at least 27 genes selected from Table 4B. In some embodiments,the combination of genes comprises at least 28 genes selected from Table4B.

TABLE 1A (12 members of module blue (Metabolism); right two columnsindicate direction of regulation): Gene ID Gene Symbol Recovery GroupOMM/PC Group 79363 RSG1 Up Down 131601 TPRA1 Up Down 100316904 SAP25 UpDown 113655 MFSD3 Up Down 161247 FITM1 Up Down 51332 SPTBN5 Up Down752014 CEMP1 Up Down 79058 ASPSCR1 Up Down 256236 NAPSB Up Down 9476NAPSA Up Down 55655 NLRP2 Up Down 25807 RHBDD3 Up Down

TABLE 1B (2 members of module black (Catabolic Metabolism); right twocolumns indicate direction of regulation): Gene ID Gene Symbol RecoveryGroup OMM/PC Group 122786 FRMD6 Down Up 7078 TIMP3 Up Down

TABLE 1C (2 members of module green (T Cell Regulation); right twocolumns indicate direction of regulation): Gene ID Gene Symbol RecoveryGroup OMM/PC Group 130399 ACVR1C Down Up 196968 DNM1P46 Down Up

TABLE 1D (2 members of module pink (Immune System Development); righttwo columns indicate direction of regulation): Gene ID Gene SymbolRecovery Group OMM/PC Group 3805 KIR2DL4 Down Up 8287 USP9Y Down Up

TABLE 1E (2 members of module turquoise (RNA Metabolism); right twocolumns indicate direction of regulation): Gene ID Gene Symbol RecoveryGroup OMM/PC Group 118932 ANKRD22 Down Up 286554 BCORP1 Down Up

TABLE 1F (2 members of module lightgreen (* GO Biological Process NotClassified); right two columns indicate direction of regulation): GeneID Gene Symbol Recovery Group OMM/PC Group 83872 HMCN1 Down Up 81491GPR63 Down Up

TABLE 1G (1 member of module cyan (Innate Immunity); right two columnsindicate direction of regulation): Gene ID Gene Symbol Recovery GroupOMM/PC Group 116071 BATF2 Down Up

TABLE 1H (1 member of module darkred (Immune Process); right two columnsindicate direction of regulation): Gene ID Gene Symbol Recovery GroupOMM/PC Group 6580 SLC22A1 Up Down

TABLE 1I (4 members of module grey (Unclustered); right two columnsindicate direction of regulation): Gene ID Gene Symbol Recovery GroupOMM/PC Group 375790 AGRN Down Up 150468 CKAP2L Down Up 285313 IGSF10Down Up 80072 HEXA-AS1 Up Down

TABLE 2 (UCLA t-test based list of 71 genes predicting day 8 organfunction recovery) Gene ID Gene Symbol Recovery Group OMM/PC Group375790 AGRN Down Up 79363 RSG1 Up Down 728431 LOC728431 Up Down 10158PDZK1IP1 Up Down 257194 NEGR1 Down Up 83872 HMCN1 Down Up 150468 CKAP2LDown Up 130399 ACVR1C Down Up 131096 KCNH8 Down Up 1237 CCR8 Down Up131601 TPRA1 Up Down 285313 IGSF10 Down Up 4311 MME Down Up 2119 ETV5 UpDown 4283 CXCL9 Down Up 1839 HBEGF Down Up 64901 RANBP17 Up Down 55510DDX43 Down Up 63914 C6orf164 Up Down 81491 GPR63 Down Up 6580 SLC22A1 UpDown 84310 C7orf50 Up Down 1E+08 SAP25 Up Down 4747 NEFL Down Up 157313CDCA2 Down Up 113655 MFSD3 Up Down 216 ALDH1A1 Down Up 10439 OLFM1 DownUp 118932 ANKRD22 Down Up 3995 FADS3 Up Down 116071 BATF2 Down Up 29901SAC3D1 Up Down 8322 FZD4 Down Up 161247 FITM1 Up Down 122786 FRMD6 DownUp 51332 SPTBN5 Up Down 348093 RBPMS2 Up Down 80072 HEXA-AS1 Up Down348110 C15orf38 Down Up 196968 DNM1P46 Down Up 752014 CEMP1 Up Down55808 ST6GALNAC1 Down Up 79643 CHMP6 Up Down 79058 ASPSCR1 Up Down220134 SKA1 Down Up 30835 CD209 Down Up 6618 SNAPC2 Up Down 558 AXL DownUp 256236 NAPSB Up Down 9476 NAPSA Up Down 3802 KIR2DL1 Down Up 3805KIR2DL4 Down Up 55655 NLRP2 Up Down 4923 NTSR1 Up Down 5413 SEPT5 UpDown 25807 RHBDD3 Up Down 7078 TIMP3 Up Down 3730 KAL1 Down Up 5638PRRG1 Down Up 7503 XIST Up Down 6192 RPS4Y1 Down Up 7544 ZFY Down Up5616 PRKY Down Up 64595 TTTY15 Down Up 8287 USP9Y Down Up 8653 DDX3YDown Up 7404 UTY Down Up 286554 BCORP1 Down Up 246126 TXLNG2P Down Up8284 KDM5D Down Up 9086 EIF1AY Down Up

TABLE 3 (UCLA Mann-Whitney-test based list of 28 genes predicting day 8organ function recovery) Gene ID Gene Symbol Recovery Group OMM/PC Group8287 USP9Y Down Up 116071 BATF2 Down Up 375790 AGRN Down Up 118932ANKRD22 Down Up 83872 HMCN1 Down Up 130399 ACVR1C Down Up 81491 GPR63Down Up 196968 DNM1P46 Down Up 150468 CKAP2L Down Up 122786 FRMD6 DownUp 3805 KIR2DL4 Down Up 2855313 IGSF10 Down Up 286554 BCORP1 Down Up100316904 SAP25 Up Down 9476 NAPSA Up Down 161247 FITM1 Up Down 51332SPTBN5 Up Down 80072 HEXA-AS1 Up Down 6580 SLC22A1 Up Down 79363 RSG1 UpDown 7078 TIMP3 Up Down 131601 TPRA1 Up Down 752014 CEMP1 Up Down 79058ASPSCR1 Up Down 113655 MFSD3 Up Down 256236 NAPSB Up Down 55655 NLRP2 UpDown 25807 RHBDD3 Up Down

TABLE 4A (12 gene overlap list of UCLA Mann-Whitney-test based list of28 genes predicting day 8 organ function recovery and predicting 1-yearsurvival) Gene ID Gene Symbol Recovery Group OMM/PC Group 116071 BATF2Down Up 375790 AGRN Down Up 118932 ANKRD22 Down Up 196968 DNM1P46 DownUp 122786 FRMD6 Down Up 3805 KIR2DL4 Down Up 286554 BCORP1 Down Up100316904 SAP25 Up Down 9476 NAPSA Up Down 80072 HEXA-AS1 Up Down 7078TIMP3 Up Down 25807 RHBDD3 Up Down

TABLE 4B (105 genes predicting 1-year survival) Regulation in 1-yearGene ID Gene Symbol survival group 375790 AGRN* down 10911 UTS2 down90853 SPOCD1 up 728431 LOC728431 up 26027 ACOT11 up 257194 NEGR1 down388646 GBP7 down 163351 GBP6 down 1952 CELSR2 down 644591 PPIAL4G up 913CD1E Down 116123 FMO9P down 8497 PPFIA4 up 400950 C2orf91 down 84279PRADC1 up 3625 INHBB down 5270 SERPINE2 up 643387 LOC643387 down 79750ZNF385D up 115560 ZNF501 down 2815 GP9 up 55214 LEPREL1 down 151963MB21D2 down 200958 MUC20 up 401115 C4orf48 up 84740 AFAP1-AS1 down152831 KLB down 677810 SNORA26 down 8492 PRSS12 down 79931 TNIP3 down7098 TLR3 down 3003 GZMK down 140947 C5orf20 down 9832 JAKMIP2 down 9509ADAMTS2 up 51149 C5orf45 up 10471 PFDN6 down 594839 SNORA33 up 84310C7orf50 up 2791 GNG11 up 100316904 SAP25* up 4747 NEFL down 1135 CHRNA2up 6129 RPL7 down 157638 FAM84B down 26149 ZNF658 down 216 ALDH1A1 down10439 OLFM1 down 1959 EGR2 down 118881 COMTD1 up 118932 ANKRD22* down619562 SNORA3 down 79080 CCDC86 down 11251 PTGDR2 down 116071 BATF2*down 55359 STYK1 down 6297 SALL2 down 122786 FRMD6* down 161291 TMEM30Bdown 100750247 HIF1A-AS2 up 8747 ADAM21 up 440278 CATSPER2P1 down 348093RBPMS2 up 595097 SNORD16 down 80072 HEXA-AS1* up 348110 C15orf38 down196968 DNM1P46* down 645811 CCDC154 up 5376 PMP22 down 400617 KCNJ2-AS1up 645158 CBX3P2 down 220134 SKA1 down 79839 CCDC102B down 284451 ODF3L2down 79187 FSD1 up 30835 CD209 down 4066 LYL1 up 773 CACNA1A down 26659OR7A5 down 126248 WDR88 up 3743 KCNA7 down 9476 NAPSA* Up 79986 ZNF702Pdown 94059 LENG9 up 3805 KIR2DL4* down 282566 LINC00515 up 9510 ADAMTS1down 11274 USP18 down 5413 SEPT5 up 100526833 SEPT5-GP1BB up 2812 GP1BBup 1415 CRYBB2 down 91353 IGLL3P down 23544 SEZ6L down 25807 RHBDD3* up7078 TIMP3* up 79924 ADM2 down 284942 RPL23AP82 down 5638 PRRG1 down27238 GPKOW down 139189 DGKK down 1741 DLG3 down 56000 NXF3 up 8653DDX3Y down 286554 BCORP1* down *indicates overlap with genes listed inTable 3FRP Scoring

In certain embodiments of methods provided herein, a functional recoverypotential (FRP) score is based on a linear discriminant analysis of thegene expression profiles on day −1 (or up to 72 hours) before the AdHFintervention that are predictive of improvement in organ functionrecovery, or “functional recovery”, after the AdHF intervention, such ascan be obtained from the information described in Tables 1-4 herein. Onecan perform this linear discriminant analysis using all 28 of the geneslisted in Table 3, a select subset, for example, of 10-20 genes shown tobe predictive of FRP. The linear discriminant analysis is adapted fromthe development of the Allomap test (Deng et al. AJT 2006:6:150), thefirst in history FDA-cleared cardiovascular in-vitro-diagnosticmultivariate index assay (IVDMIA) test to assist the clinician in rulingout heart transplantation rejection, to select genes and/or metagenesthat, in combination, optimally predict functional recovery. As moredata are gathered, for example, following completion of a planned ≥1000patient FDA-Pivotal Trial, the functional recovery potential (FRP) canbe refined further according to the rationale described in Deng, M C, Aperipheral blood transcriptome biomarker test to diagnose functionalrecovery potential in advanced heart failure. Biomark Med. 2018 May 8.doi: 10.2217/bmm-2018-0097. Such further refinement includes, forexample, weighting the contribution of individual genes to the FRP scoreas the analysis reveals which genes have greater predictive value.

Thus, the invention provides a method for developing a function recoverypotential (FRP) scoring algorithm that predicts a subject's ability torecover from medical intervention for organ failure. In one embodiment,the method comprises (a) obtaining the expression levels of at least 10of the 28 genes listed in Table 3 using pre-intervention andpost-intervention expression levels of the at least 10 genes observed inPBMC samples obtained from a population of patients treated with medicalintervention for organ failure; (b) performing linear discriminantanalysis of the expression levels obtained in (a) to classify the PBMCsamples into Group I (post-intervention improvement) or Group II(non-improvement); (c) estimating the effect size of each of the geneexpression levels on the classification of a sample into Group I orGroup II; and (d) adjusting the FRP scoring algorithm by weighting thecontribution of each of the genes in accordance with the effect size.Estimating the effect size can comprise, for example, determining theeigenvalue for each gene, or it can be based on the canonicalcorrelation.

As described in the Examples below, and now published as Bondar, G. etal., PLoS One 2017 Dec. 13; 12(12), one can construct aPBMC-GEP-prediction model using preoperative day −1 patient data topredict and classify postoperative Group I (functional recovery; lowrisk; high FRP score) vs. Group II (no recovery of organ function; highrisk; low FRP score). In the proof of concept study (Bondar, G. et al.2017 cited above), to achieve a prediction model with highest accuracyfor classification of patients into Group I vs. Group II, Strand NGSv2.9 was used for the alignment and analysis of the RNA-Seq data. Afteralignment, DESeq normalization, filtering and fold change analysis ofgenes expressed above noise levels resulted in 28 genes.

The 28 PBMC-genes that are differentially expressed between Group I andGroup II were identified by non-parametric statistics (Mann-Whitney testwith Benjamini-Hochberg correction). Since the original False DiscoveryRate (FDR) methodology is too conservative for genomics applications andresults in a substantial loss of power, we used a more relaxed criteria(FDR≤0.1). Only those genes with fold change of at least 2.0 wereincluded in the analysis. Biological significance was assessed usinggene ontology, pathway analysis and via GeneCards database. The list of28 genes was then used to build the model to classify postoperativeGroup I vs. Group II. We constructed this prediction model onpreoperative day −1 gene expression data using the support vectormachine (SVM) algorithm. Out of 29 samples, 20 were randomly selected tobuild the model and the remaining 9 samples, stratified by membership inGroup I or Group II, were used to test the model. The prediction modelwas tested on 25 repetitions with random sampling. Hence, the model wasbuilt on a 20×28 matrix. Testing of the model showed prediction of GroupI versus Group II membership with 93% accuracy. One-year survival inGroup I was 15/17 and in Group II 3/11, indicating lower risk in Group I(Fisher's Exact Test p<0.005). Importantly, the time-to-eventKaplan-Meier survival analysis suggested that the significantly elevatedrisk of death in Group II vs. Group I continued over the 1-year periodfollowing MCS-surgery (log rank p=0.00182).

In one illustrative example, a subject's blood sample is assayed forexpression levels of the 12 genes listed in Table 4. The amount of geneexpression is determined relative to a reference value. The referencevalue is the level of a normalization gene (one known NOT to be relatedto FRP) and/or it can be a level known to be representative of healthyindividuals and/or individuals known to recover from heart failure. Aneutral score on the 10-point FRP scale would be 5.5. The averagefold-change in gene expression would contribute to an increase ordecrease from 5.5, depending on whether it was in the direction ofchange associated with recovery, to arrive at the FRP score for thatsubject. As shown in Table 4, down-regulation of the first seven genesis associated with recovery from heart failure, while up-regulation ofthe last five genes is associated with recovery. Optionally, thecontribution of each gene's expression level is weighted based on thelinear discriminant analysis to adjust for differences amongst genes intheir predictive value.

Those skilled in the art will recognize that the FRP scale can beexpressed on the basis of other numerical ranges and still operate inthe same manner as the 10-point scale described herein. For example, theFRP scale can be a 0-5 point range, a 0-50 point range, or a 0-100 pointrange. Deviation relative to a neutral midpoint can still be calculatedin a manner that is based on the relative expression levels of the geneslisted in Tables 1-4, and adjusted to take into account appropriateweighting and other parameters considered predictive of functionalrecovery.

Kits and Assay Standards

In some embodiments the invention provides kits for measuring geneexpression for one or more of the genes provided in Tables 1-4. Somesuch kits comprise a set of reagents as described herein thatspecifically bind one or more genes of the invention, and optionally,one or more suitable containers containing reagents of the invention. Insome embodiments, reagents herein specifically bind to at least one genecomprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1,NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5,CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1,ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, or FITM1.Reagents include molecules that specifically bind one or more genes orgene products of the invention, including primers and probes. Reagentscan optionally include a detectable label. Labels can be fluorescent,luminescent, enzymatic, chromogenic, or radioactive.

Kits of the invention optionally comprise an assay standard or a set ofassay standards, either separately or together with other reagents. Anassay standard can serve as a normal control by providing a referencelevel of normal expression for a given marker that is representative ofa healthy individual.

Kits can include probes for detection of alternative gene expressionproducts The kit can optionally include a buffer. While some embodimentsuse the NGS-platform, other embodiments use qPCR/Nanostring/Nanoporetechnology.

In some embodiments the invention provides for the establishment of oneor more central laboratories to which patient blood samples can beshipped for assay using polymerase chain reaction (PCR), next generationsequencing (NGS), or other gene expression profiling assay for one ormore of the genes provided in Tables 1-4.

Computer Implementations

Provided herein, in certain aspects, are computer implemented systemsfor use in methods herein, such as methods of treatment, methods of geneexpression profiling, and methods of recommending a treatment (FIG. 2).In some embodiments, computer implemented systems herein comprise: (a) asample receiver for receiving a sample provided by an individual; (b) adigital processing device comprising an operating system configured toperform executable instructions and a memory; and (c) a computer programincluding instructions executable by the digital processing device toprovide a treatment to a healthcare provider based on the sample. Insome embodiments, the computer program comprises: (i) an gene analysismodule configured to determine a gene expression level in the sample forat least one gene comprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5,CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C,DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1,AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8,MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2,ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1,CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1,XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, orFITM1; (ii) a treatment determination module configured to determine thetreatment based on the gene expression level; and (iii) a display moduleconfigured to provide the treatment to the healthcare provider.

The invention provides a non-transitory computer-readable medium encodedwith computer-executable instructions for performing the methodsdescribed herein. In another embodiment, the invention provides anon-transitory computer-readable medium embodying at least one programthat, when executed by a computing device comprising at least oneprocessor, causes the computing device to perform one or more of themethods described herein. In some embodiments, the at least one programcontains algorithms, instructions or codes for causing the at least oneprocessor to perform the method(s). Likewise, the invention provides anon-transitory computer-readable storage medium storingcomputer-readable algorithms, instructions or codes that, when executedby a computing device comprising at least one processor, cause orinstruct the at least one processor to perform a method describedherein.

Those of ordinary skill in the art would understand that the variousembodiments of the method described herein, including analysis of geneexpression profiles, generation of FRP scores, and prediction ofoutcomes, for example, can be implemented in electronic hardware,computer software, or a combination of both (e.g., firmware). Whetherthe present method is implemented in hardware and/or software may dependon, e.g., the particular application and design constraints imposed onthe overall system. Ordinary artisans can implement the present methodin varying ways depending on, e.g., particular application and designconstraints, but such implementation decisions do not depart from thescope of the present disclosure.

The computer programs/algorithms for performing the present method canbe implemented with, e.g., a general-purpose processor, a digital signalprocessor (DSP), an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or other programmable logicdevice, discrete gate or transistor logic, discrete hardware components,or any combination thereof designed to perform the functions and stepsdescribed herein. A general-purpose processor can be a microprocessor,but alternatively the processor can be any conventional processor,controller, microcontroller or state machine. A processor can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of the present method, or the computer programs/algorithms forperforming the method, can be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of hardwareand software (e.g., firmware). A software module can reside in, e.g.,RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, a hard drive, a solid-state drive, a removable disk or disc,a CD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such that theprocessor can read information from, and write information to, thestorage medium. Alternatively, the storage medium can be integral to theprocessor. The processor and the storage medium can reside in, e.g., anASIC, which in turn can reside in, e.g., a user terminal. In thealternative, the processor and the storage medium can reside as discretecomponents in, e.g., a user terminal.

In one or more exemplary designs, the functions for carrying out themethod described herein can be implemented in hardware, software,firmware or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over a computer-readablemedium as instructions or codes. Computer-readable media include withoutlimitation computer storage media and communication media, including anymedium that facilitates transfer of a computer program/algorithm fromone place to another. A storage medium can be any available medium thatcan be accessed by a general-purpose or special-purpose computer orprocessor. As a non-limiting example, computer-readable media cancomprise RAM, ROM, EEPROM, CD-ROM or other optical disc storage,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to carry or store a computer program/algorithmin the form of instructions/codes and/or data structures and that can beaccessed by a general-purpose or special-purpose computer or processor.In addition, any connection is deemed a computer-readable medium. Forexample, if the software is transmitted from a website, a server orother remote source using a coaxial cable, fiber optic cable, twistedpair, digital subscriber line (DSL), or a wireless technology such asinfrared, radio wave or microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technology such as infrared, radiowave or microwave are computer-readable media. Discs and disks includewithout limitation compact disc (CD), laser disc, optical disc, digitalversatile disc (DVD), blu-ray disc, hard disk and floppy disk, wherediscs normally reproduce data optically using a laser, while disksnormally reproduce data magnetically. Combinations of the above are alsoincluded within the scope of computer-readable media.

The methods described herein can be automated. Accordingly, in someembodiments the method is implemented with a computer system (e.g., aserver, a desktop computer, a laptop, a tablet or a smartphone)comprising at least one processor. The computer system can be configuredor provided with algorithms, instructions or codes for performing themethod which are executable by the at least one processor. The computersystem can generate a report containing information on any or allaspects relating to the method, including results of the analysis of thebiological sample from the subject. The disclosure further provides anon-transitory computer-readable medium encoded with computer-executableinstructions for performing the present method.

FIG. 2 is a block diagram of an embodiment of a computer system 200 thatcan be used to implement a method as described herein. System 200includes a bus 208 that interconnects major subsystems such as one ormore processors 210, a memory subsystem 212, a data storage subsystem214, an input device interface 216, an output device interface 218, anda network interface 220. Processor(s) 210 perform many of the processingfunctions for system 200 and communicate with a number of peripheraldevices via bus 208.

Memory subsystem 212 can include, e.g., a RAM 232 and a ROM 234 used tostore codes/instructions/algorithms and data that implement variousaspects of the present method. Data storage subsystem 214 providesnon-volatile storage for program codes/instructions/algorithms and datathat implement various aspects of the present method, and can include,e.g., a hard disk drive 242, a solid-state drive 244, and other storagedevices 246 (e.g., a CD-ROM drive, an optical drive, a removable-mediadrive, and so on). Memory subsystem 212 and/or data storage subsystem214 can be used to store, e.g., the gene expression profile and the FRPscore of subjects, and the therapeutic outcome of treatment of thosesubjects with a particular AdHF or other intervention. Thecodes/instructions/algorithms for implementing certain aspects of thepresent method can be operatively disposed in memory subsystem 212 orstored in data storage subsystem 214.

Input device interface 216 provides interface with various inputdevices, such as a keyboard 252, a pointing device 254 (e.g., a mouse, atrackball, a scanner, a pen, a tablet, a touch pad or a touch screen),and other input device(s) 256. Output device interface 218 provides aninterface with various output devices, such as a display 262 (e.g., aCRT or an LCD) and other output device(s) 264. Network interface 220provides an interface for computer system 200 to communicate with othercomputer systems coupled to a network to which system 200 is coupled.

Other devices and/or subsystems can also be coupled to computer system200. In addition, it is not necessary for all of the devices andsubsystems shown in FIG. 2 to be present to practice the methoddescribed herein. Furthermore, the devices and subsystems can beinterconnected in configurations different from that shown in FIG. 2.

EXAMPLES

The following examples are presented to illustrate the present inventionand to assist one of ordinary skill in making and using the same. Theexamples are not intended in any way to otherwise limit the scope of theinvention.

Example 1: Association Between Preoperative PBMC Gene ExpressionProfiles, Early Postoperative Organ Function Recovery Potential, andLong-Term Survival in Advanced Hearth Failure Patients UndergoingMechanical Circulatory Support

This Example demonstrates that preoperative PBMC-GEP predicts earlychanges in organ function scores and correlates with long-term outcomesin AdHF patients following MCS implantation. Therefore, gene expressionlends itself to outcome prediction and warrants further studies inlarger longitudinal cohorts.

Heart failure (HF) is a complex clinical syndrome that results from anystructural or functional cardiovascular disorder causing a mismatchbetween demand and supply of oxygenated blood and consecutive failure ofthe body's organs. In the United States, HF affects about 6 millionpersons [1]. HF with reduced ejection fraction (HFrEF) affects 3 millionpeople [2]. The lifetime risk of developing HF for men and women olderthan 40 years of age is 1 in 5. The death rate remains unacceptably highat approximately 50% within 5 years from time of initial diagnosis.Stage D, or advanced heart failure (AdHF), designates patients withtruly refractory HF (estimated at 300,000 persons in the US annually)[2].

AdHF patients may benefit from the following therapeutic options:optimal medical management (OMM) or palliative/hospice care (PC,n=300,000), mechanical circulatory support (MCS, n=30,000) or hearttransplantation (HTx, n=3,000) [3]. MCS devices, originally used forpatients with AdHF as a bridge-to-transplant or bridge-to-recovery, arenow increasingly used as destination (lifelong) therapy and have thepotential to outnumber HTx by a factor of 1:10, currently showing animproved survival rate of approximately 80% at 1 year [4].

Because of this success, destination MCS is increasingly being offeredto patients with challenging clinical profiles. There is significantpatient-to-patient variability for risk of adverse events, includingdeath, after MCS-surgery. The ability to preoperatively predict thisrisk for the individual AdHF-patient before surgery and the impact ofthis risk on the associated long-term survival prognosis would be a veryimportant component of clinical decision-making and management.Currently, we have our clinical expertise and validated clinical tools[4-18] for risk prediction.

However, despite our clinical expertise and validated tools, it is noteasy to assess this risk and, therefore, make recommendations aboutwhich therapy benefits the individual patient most with respect tolong-term survival. Often, elderly and frail AdHF patients, if not doingwell on OMM, are also at increased risk for organ dysfunction (OD) anddeath after MCS-surgery. One of the reasons for the current challengesof risk prediction is the difficulty in assessing the degree of frailtyand OD in the individual AdHF patient who often suffers frommalnutrition, immune dysfunction, and poor infection coping potential.

Preoperative HF-related immunologic impairment is a component of pooroutcomes after MCS and HTx, owing to the known associations betweenincreased age, T cell and innate immune cell dysfunction, frailty,increased numbers of terminally differentiated T cells, immunesenescence (deficient replicative ability), and immune exhaustion(impaired antigen response) [19-23]. Multi-organ dysfunction syndrome(MOD) is one of the leading causes of morbidity and mortality. It isassociated with grossly aberrant immune activation [4-18, 24-26].

None of the current established clinical scoring and prediction toolsintegrate immune function parameters. They have the tendency to beimprecise in estimating risk among severely ill patients [11, 12],making the therapeutic recommendation with the best survival estimatefor the individual patient very difficult. Our central postulate is thatOD and patient death after MCS- or HTx-surgery results from innate andadaptive immune cell dysfunction. Therefore, our goal is to useleukocyte immune-biology information to develop a preoperative test,which would precisely predict postoperative outcomes in the individualAdHF patient. We utilized the widely accepted Sequential Organ FailureAssessment (SOFA) [27] and Model of End Stage Liver Disease without INR(MELD-XI) [24, 28, 29] scores as quantitative assessment tools tointerpret the PBMC data and to develop a predictive leukocyte biomarker.

In order to achieve this goal, we hypothesize that in AdHF patientsundergoing MCS-surgery, HF-related preoperative peripheral bloodmononuclear cell (PBMC) gene expression profiles (GEP) correlate withand predict changes of early postoperative organ function status assurrogates for 1 year survival.

In our prior studies, we reported on PBMC GEP time course analyses afterMCS-surgery [30-32]. Here, we present data to support our hypothesesthat, in AdHF patients undergoing MCS implantation, preoperativedifferential PBMC-GEP are associated with and are predictive of earlypostoperative SOFA and MELD-XI score changes, defined as scoredifference between immediately before surgery to 8 days after surgery asa surrogate marker for long term mortality risk.

The findings support the concept of developing a Functional RecoveryPotential (FRP), seen as a person's quantifiable potential to improveafter being exposed to a stressor, such as MCS-surgery.

Methods & Design

Study Design

To address the most pressing clinical problem of MCS-relatedperioperative MOD [4, 33, 34], we chose to base this analysis on acontrol population of AdHF-patients undergoing MCS-surgery alone. Weconducted a study with 29 AdHF patients undergoing MCS-surgery at UCLAMedical Center between August 2012-2014 under UCLA Medical InstitutionalReview Board 1 approved Protocol Number 12-000351. Written informedconsent was obtained from each participant.

Clinical Management.

All study participants were referred to the UCLA Integrated AdHF Programand evaluated for the various therapeutic options, including OMM, MCS,and HTx. All patients were optimized regarding HF therapy, consented toand underwent MCS-therapy according to established guidelines [35, 36],based on the recommendations of the multidisciplinary heart transplantselection committee.

After anesthesia induction, patients were intubated and placed oncardiopulmonary bypass. The type of MCS-device selected depended on theacuity and severity of the heart failure syndrome, as well as patientcharacteristics [37]. For left ventricular support, patients underwenteither Heartmate II (HeartMate II® pumps are valveless, rotary,continuous flow pumps) or HVAD (HeartWare® HVAD pumps are valveless,centrifugal, continuous flow pumps). For biventricular support, patientsunderwent either Centrimag-BVAD (Centrimag® pumps are valveless,centrifugal, continuous flow pumps that are external to the body), PVADbiventricular assist device (BVAD) (Thoratec® Paracorporeal VentricularAssist Device (PVAD) pumps each contain two mechanical tilting diskvalves) or the t-TAH (the Temporary Total Artificial Heart consists oftwo artificial ventricles that are used to replace the failing heart).

Various combinations of cardiovascular inotropic and vasoactive drugswere used to support patient's hemodynamics postoperatively, tailored tothe individual requirements. In addition, other temporary organ systemsupport was administered as required (e.g. mechanical ventilation,hemodialysis, blood transfusions, and antibiotic therapy).

Clinical Phenotyping.

Demographic variables were obtained for all patients. Twelve distinctparameters were collected on a daily basis for time-dependent clinicalphenotyping of the patient cohort, which included serum bilirubin, serumcreatinine, leukocyte count, platelet count, alveolar oxygen pressure,fraction of inspired oxygen (FiO2), mean systemic arterial pressure(MAP), INR (International Normalized Ratio, for prothrombin time), bloodglucose, heart rate, respiratory rate, temperature, and the Glasgow ComaScale (GCS).

Using combinations of these parameters, we also calculated two validatedand commonly used composite OD scores, SOFA [27] and MELD-XI [24]. TheSOFA score is a validated and widely accepted measure that rates degreeof organ failure across six major organ systems (cardiovascular,respiratory, neurological, renal, hepatic, and coagulation). The MELD-XIscore is a variation of the MELD score that uses only the bilirubin andcreatinine levels, and eliminates the INR, which is typically notinterpretable in these patients given the need of anticoagulation.

We also used the Interagency Registry for Mechanically AssistedCirculatory Support (INTERMACS) scoring system, which has been developedto improve patient selection and timing of MCS therapy [4] forpreoperative HF-severity assessment. Higher INTERMACS risk categoriesare considered predictors of worse survival. While INTERMACS identifiesclinical outcomes and risk of MOD, it does not provide insights into theunderlying immunological mechanisms of disease.

Clinical Outcome Parameter.

One of the most significant clinical outcome parameters for AdHFpatients undergoing MCS is the probability of organ function improvementfrom one day before to eight days after surgery.

From a clinical utility perspective, we aim to provide AdHF-patientswith the most precise prediction of short- and long-term outcome [38,39] on either OMM or MCS. Since many AdHF patients have varying recoverypotential, we chose a short-term improvement criteria, i.e. 8 dayspostoperatively, as a surrogate outcome parameter for long-termsurvival. For these reasons, we chose not to use a static preoperativeorgan function severity score to develop our biomarker. The most logicalclinical parameter is the potential for organ function improvement,which we named the short-term functional recovery potential (FRP). Thisparameter may identify patients who benefit from aggressive therapies,such as MCS, even if they are very ill.

Therefore, patients were grouped into two organ failure risk strata:Group I=improving (both SOFA and MELD-XI scores improve from day −1 today 8) and Group II=not improving (SOFA and/or MELD-XI score(s) do notimprove from day −1 to day 8). In other words, if the MCS-surgeryimproves the hemodynamic situation without complications, then thepatient's organ function is expected to recover by postoperative day 5and clearly by postoperative day 8, which should be reflected in aconcordant improvement of SOFA and MELD-XI score, from day −1 to day 8.On the other hand, if SOFA or MELD-XI, or both, scores do not improvefrom day −1 to day 8, we hypothesize that this problem may potentiallyimpact long-term survival.

PBMC Sample Processing & GEP Protocol.

PBMC samples were collected one day before surgery (day −1). Clinicaldata was collected on day −1 and day 8 postoperatively. We chose, basedon our successful Allomap™ biomarker test development experience[40-43], to focus on the mixed PBMC population.

Eight ml of blood was drawn into a CPT tube (Becton Dickinson, FranklinLakes, N.J.). Peripheral Blood Mononuclear cells (PBMC) from each samplewere purified within 2 h of phlebotomy. The collected blood was mixedand centrifuged at room temperature (22° C.) for 20 min at 3000 RPM. Twoml of plasma was separated without disturbing the cell layer into aneppendorf tube (Sigma-Aldrich, St. Louis, Mo.) and stored at −80° C. forfuture experiments. The cell layer was collected, transferred to 15 mlconical tubes and re-suspended in cold Phosphate Buffer Saline (PBS)(Sigma-Aldrich, St. Louis, Mo.) and centrifuged for 20 min at 1135 RPMat 4° C. The supernatant was aspirated and discharged. The cell pelletwas re-suspended in cold PBS, transferred into an eppendorf tube andcentrifuged for 20 min at 5.6 RPM at 4° C. The supernatant wasdischarged. The pellet was re-suspended in 0.5 ml RNA Protect CellReagent (Qiagen, Valencia, Calif.) and frozen at −80° C.

PBMC Transcriptome RNA Sequencing.

All samples were processed using next-generation RNA sequencingtranscriptome analysis at the UCLA Technology Center for Genomics &Bioinformatics. Briefly, the RNA was isolated from the PBMC using RNeasyMini Kit (Qiagen, Valencia, Calif.). The quality of the total RNA wasassessed using NanoDrop® ND-1000 spectrophotometer (NanoDropTechnologies, Wilmington, Del.) and Agilent 2100 Bioanalyzer (AgilentTechnologies, Palo Alto, Calif.) concentration above 50 ng/μl.,purity—260/280˜2.0, integrity—RIN>9.0 and average >9.5. Then, mRNAlibrary was prepared with Illumina TruSeq RNA kit according to themanufacturer's instructions (Illumina, San Diego, Calif.). Libraryconstruction consists of random fragmentation of the polyA mRNA,followed by cDNA production using random polymers. The cDNA librarieswere quantitated using Qubit and size distribution was checked onBioanalyzer 2100 (Agilent Technologies, Palo Alto, Calif.). The librarywas sequenced on HiSeq 2500. Clusters were generated to yieldapproximately 725K-825K clusters/mm2. Cluster density and quality wasdetermined during the run after the first base addition parameters wereassessed. We performed single end sequencing runs to align the cDNAsequences to the reference genome. Generated FASTQ files weretransferred to the AdHF Research Data Center where Avadis NGS 1.5(Agilent, Palo Alto, Calif. and Strand Scientific, CA) was used to alignthe raw RNA-Seq FASTQ reads to the reference genome. After RNAextraction, quantification and quality assessment, total mRNA wasamplified and sequenced on the whole-genome Illumina HiSeq 2500. Datawas then subjected to DeSeq normalization using NGS Strand/Avadis (v2.1Oct. 10, 2014). Batch effects were removed using the ComBat algorithm inR [44].

Statistical Analysis

Transcriptome Analysis.

We were interested in finding the preoperatively differentiallyexpressed genes (DEG) in the GEP of 29 patients, as they correlate toearly postoperative organ function improvement as markers for long-termsurvival outcome. PBMC-genes differentially expressed between Group Iand Group II were identified by non-parametric statistics (Mann-Whitneytest with Benjamini-Hochberg correction). Since the original FalseDiscovery Rate (FDR) methodology [45] is too conservative for genomicsapplications and results in a substantial loss of power [46], we used amore relaxed criteria (FDR≤0.1) values as an exploratory guide for whichentities to investigate further. Only those genes with fold change of atleast 2.0 were included in the analysis. Biological significance wasassessed using gene ontology, pathway analysis and via GeneCardsdatabase.

Prediction Model Building and Testing.

To classify postoperative Group I vs. Group II, we constructed aPBMC-GEP prediction model on preoperative day −1 gene expression datausing the support vector machine (SVM) algorithm. Out of 29 samples, 20were randomly selected to build the model and the remaining 9 samples,stratified by membership in Group I or Group II, were used to test themodel. The prediction model was tested on 25 repetitions with randomsampling.

Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) Validation.

NGS data were validated by Quantitative PCR obtained from PBMC of 6samples taken across Group I (n=3) and Group II (n=3). Total RNA fromPBMC were purified using RNeasy Mini Kit (Qiagen, Valencia, Calif.).CDNA was synthesized with iScript supermix for RT-qPCR (BioRad,Hercules, Calif.). RT-qPCR analysis was carried out with iTaq SYBR greensupermix (BioRad, Hercules, Calif.) on the 7500 Fast Real-time PCRsystem (Applied Biosystems, Foster City, Calif.). GAPDH levels were usedas an internal control for RT-qPCR. Sequences of the primer pairs usedwere as follows: GAPDH-f: CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r:ACCCTGTTGCTGTAGCCA (SEQ ID NO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQID NO: 3); KIR2DL4-r: ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f:CAGGACACCTGGGTTCACAC (SEQ ID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQID NO: 6); BATF2-f: AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r:TCTTTTTCCAGAGACTCGTGC (SEQ ID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG(SEQ ID NO: 9); ANKRD22-r: TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).

Results

Clinical Profiles and Outcomes

Pre-, Intra- and Postoperative Clinical Profiles and Long-Term Survival.

Out of 29 patients, 17 were preoperatively in INTERMACS class 1-2 (astate of critical cardiogenic shock or progressively declining oninotropic support), while the remaining 12 patients were in INTERMACSclass 3-4 (inotrope dependent or resting symptoms) [4]. Characteristicsof the patients are shown in Table 1 of Bondar, G. et al., 2017, PLoSONE 12(12): e0189420. The SOFA and MELD-XI OD trajectory for each groupis summarized in FIG. 3A. The same data in terms of amount ofimprovement is shown in FIG. 3B. One-year survival in Group I was 15/17and in Group II 3/11, indicating lower risk in Group I (Fisher's ExactTest p<0.005). Importantly, the time-to-event Kaplan-Meier survivalanalysis suggested that the significantly elevated risk of death inGroup II vs. Group I continued over the 1-year period followingMCS-surgery (log rank p=0.00182; FIG. 4).

Neither correlation between preoperative clinical variables (i.e.INTERMACS class, SOFA median score, MELD-XI median score, and SeattleHeart Failure Model, excluding respiratory rate) nor intra/postoperativeclinical variables predict Group I versus Group II membership or year 1survival status. We grouped preoperative right ventricular function,defined by echocardiographic criteria, into two groups: normal to mildlyreduced right ventricular function (n=12) and moderately to severelyreduced right ventricular function (n=17). The chi-square p-value forpostoperative Group I versus II membership was non-significant (p=0.42).We grouped preoperative inotrope support levels into the followingcategories: no inotrope (n=7), 1 inotrope (n=3), 2 inotropes (n=11), >3inotropes or MCS (e.g. VA ECMO) (n=8). The chi-square p-value forpostoperative Group I versus II membership was non-significant (p=0.61).Additional preoperative clinical information data (i.e. bilirubin,creatinine, international normalized ratio, white blood cells, heartrate, and glucose level, all non-significant chi-square p-value)(respiratory rate, p=0.03) are also summarized in Table 1. None of the29 patients had a clinical infection episode on the day prior to MCSsurgery.

The intraoperative median cardiopulmonary bypass (CPB) time was 107 min(25%/75%: 75 min/145 min). We categorized patient CPB time into twogroups: patients with no CPB (e.g. minimally invasive LVAD-placement) orCPB time shorter than the median time (n=15) and patients with CPB timeequal to or longer than the median time (n=14). The chi-square p-valuefor Group I versus II membership was non-significant (p=0.51).Additionally, the group without major intraoperative bleeding (n=20) wascompared to those patients with major bleeding (n=9). Bleeding severitywas defined per INTERMACS criteria as greater than or equal to 4 RBC perany 24 h period during the first 8 postoperative days. The chi-squarep-value for postoperative Group I versus II membership wasnon-significant (p=0.06).

Out of 11 patients who died postoperatively, 9 patients died from MOD, 1patient from gastro-intestinal hemorrhage and 1 patient from sepsis.

Correlation Between Preoperative PBMC-Transcriptome and ClinicalOutcomes

PBMC-Transcriptome and Clinical Course.

Out of 29 patients undergoing MCS-surgery, 17 were in Group I and 12 inGroup II. Twenty-eight MCS-surgery patients were alive 8 dayspostoperatively. Since our study explored how the preoperativePBMC-transcriptome can predict postoperative clinical outcomes, werestricted our analysis to the relationship between preoperative day −1PBMC data and change of clinical data from preoperative day −1 topostoperative day 8. This project is based on our previously publishedstudies that characterized the postoperative correlation between PBMCGEP and clinical parameters [30, 31], as well as our time-courseanalysis of the correlation between PBMC GEP module eigengenome andclinical parameters [32].

Preoperative PBMC-Transcriptome and Early Postoperative Organ FunctionChanges.

In order to identify day −1 transcripts related to organ functionchange, the entire set of mRNA transcripts (36,938) was filtered(20th-100th percentile) [44]. Of the resulting 26,571 entities, onlythose with a fold change of at least 2.0 (123 transcripts) were retainedfor statistical analysis with the unpaired Mann-Whitney test andBenjamini-Hochberg correction analysis (FDR=0.1). After these filteringsteps, 28 genes were identified as differentially expressed between thetwo groups on day −1 (FIG. 5A, Table 5).

Preoperative PBMC-Transcriptome and 1-Year Outcome.

Eighteen out of 29 patients were alive after 1 year while 11/29 diedduring year 1. The causes of death are summarized in Table 1 of Bondar,G. et al. 2017. The preoperative GEP was different in year 1 survivorsand non-survivors. The filtered 25,319 entities were analyzed using 2.0fold change criteria. The 177 differentially expressed genes wereanalyzed by unpaired Mann-Whitney test with Benjamini-Hochbergcorrection, resulting in 105 transcripts (FDR=0.1). Hierarchicalclustering was used on the 105 differentially expressed genes(Corresponding 105 genes in Table 4B and publication Bondar et al 2017)for the year 1 survival patients (FIG. 5B). Out of these genes, 12overlap with the 28 genes that correlated with day 8 organ functionimprovement (FIG. 5C, Table 5).

Out of the 28 genes that were differentially expressed between the twogroups (Group I vs Group II membership) on postoperative day 8, 12 genesoverlapped with 1-year survival status (blue rows).

TABLE 5 Known Function of 28 Gene Classifier ENTREZ Gene Fold p (corr)ID Symbol Change GROUP I GROUP II p-value with FDR Gene Summary 8287USP9Y −6.442405 DOWN UP 0.01792041 0.09173012 USP9Y is associated toUbiquitin- Proteasome Dependent Proteolysis, and essential component ofTGF- beta/BMP signaling cascade. Within nondiabetic heartfailure-associated genes with ischemic cardiomyopathy, it was shown tohave a high degree of upregulation 116071 BATF2 −2.2702124 DOWN UP0.01606008 0.09173012 BATF2 controls the differentiation oflineage-specific cells in the immune system. Following infection,participates in the differentiation of CD8(+) thymic conventionaldendritic cells in the immune system. Selectively suppresses CYR61/CCN1transcription and hence blocks the downstream cell proliferation signalsproduced by CYR61 and inhibits CYR61-induced anchorage-independentgrowth and invasion in several cancer types; IFNs, apart from theirfunction as antiviral infection agents, exert a variety of inhibitoryeffects on cell growth, apoptosis, and angiogenesis. IFNs induce growthinhibition by a variety of pathways that involve many IFN-stimulatedgenes BATF2 is one of these genes and can be induced by IFNb, whichindicates that BATF2 may be a key component involved in IFN signaling.375790 AGRN −2.2644913 DOWN UP 0.0173365 0.09173012 AGRN is responsiblefor the maintenance of neuromuscular junction (NMJ) and directs keyevents in postsynaptic differentiation 118932 ANKRD22 −2.685126 DOWN UP0.02013588 0.09173012 ANKRD22 shows the highest upregulation with avalue of 3.06 in the RT-qPCR analysis in finding diagnostic biomarkersin Pancreatic Adenocarcinoma Patients. The function of ANKRD22 remainsunknown, but it has been patented by Rosenthal et al. as a possiblebiomarker for several types of cancer and by Brichard et al. foridentification of the patient response to cancer immunotherapy 83872HMCN1 −2.608948 DOWN UP 0.00819846 0.09173012 HMCN1 encodes a largeextracellular member of the immunoglobulin superfamily it is associatedwith Age-Related and Postpartum Depression 130399 ACVR1C −2.2228224 DOWNUP 0.00538353 0.09173012 ACVR1C is a type I receptor for the TGFB, Playsa role in cell differentiation, growth arrest and apoptosis. 81491 GPR63−2.2556078 DOWN UP 0.00260203 0.09173012 GPR63 is a G-protein coupledreceptor activity and plays a role in brain function. 196968 DNM1P46−2.2071562 DOWN UP 0.00731407 0.09173012 DNM1P46 is a pseudogene.Although not fully functional, pseudogenes may be functional, similar toother kinds of noncoding DNA, which can perform regulatory functions.150468 CKAP2L −2.7842844 DOWN UP 0.00412917 0.09173012 CKAP2L is amicrotubule-associated protein. 122786 FRMD6 −2.4180312 DOWN UP0.00285335 0.09173012 FRMD6 is a Protein Coding gene. Among its relatedpathways are Cytoskeletal Signaling and Hippo signaling pathway 3805KIR2DL4 −3.4912138 DOWN UP 0.00362563 0.09173012 KIR2DL4 is part of thekiller cell immunoglobulin-like receptors (KIRs) which are transmembraneglycoproteins expressed by natural killer cells and subsets of T cells.Inhibits the activity of NK cells thus preventing cell lysis. Unlikeclassic HLA class I molecules. HLA-G does not seem to possesssignificant immune stimulatory functions, and even responses directedagainst allogeneic HLA-G have not been reported. HLA-G, however,possesses the capability common to HLA class I molecules, to bindinhibitory receptors (FIG. 1C). Three HLA-G receptors have beendescribed: ILT2/CD85j/LILRB1 (ILT2), ILT4/CD85d/LILRB2 (1LT4), andKIR2DL4/CD158d (KIR2DL4) 285313 IGSF10 −3.154924 DOWN UP 0.009961860.09173012 IGSF10 (Immunoglobulin Superfamily Member 10) is a ProteinCoding gene 286554 BCORP1 −3.629981 DOWN UP 0.0200527 0.09173012 BCORP1is a pseudogene. Although not fully functional, pseudogenes may befunctional, similar to other kinds of noncoding DNA, which can performregulatory functions. 100316904 SAP25 2.371788 UP DOWN 0.006945670.09173012 SAP25 is a new member of the growing family ofnucleocytoplasmic shuttling proteins that are located in PML nuclearbodies. PML nuclear bodies are implicated in diverse cellular functionssuch as gene regulation, apoptosis, senescence, DNA repair, andantiviral response. Involved in the transcriptional repression 9476NAPSA 2.1895149 UP DOWN 0.01598942 0.09173012 NAPSA is a pronapsin Awhich may have considerable diagnostic value as a marker for primarylung cancer. In contrast, the pronapsin B gene, which lacks an in-framestop codon and so may be a transcribed pseudogene, is expressed atcomparable levels in normal human spleen, thymus, cytotoxic and helperTlymphocytes, natural killer (NK) cells and Blymphocytes; may alsofunction in protein catabolism 161247 FIT1 2.272873 UP DOWN 0.009385060.09173012 FIT1 in skeletal muscle and FIT2 in adipose, it isinteresting to speculate that FIT1 might be essential for the rapidoxidation of FAs stored as TG in LDs while FIT2 is required for thelongterm storage of TG in adipocytes. Plays an important role in lipiddroplet accumulation. 51332 SPTBN5 2.3029516 UP DOWN 0.014341270.09173012 SPTBN5 is related to pathways of Interleukin-3, 5 and GM-CSFsignaling and Signaling by GPCR 80072 HEXA-AS1 2.1215222 UP DOWN0.01862884 0.09173012 SPTBN5 (Spectrin Beta, Non- Erythrocytic 5) is aProtein Coding gene. HEXA-AS1 (HEXA Antisense RNA 1) is an RNA Gene, andis affiliated with the antisense RNA class. 6580 SLC22A1 2.033342 UPDOWN 0.0173365 0.09173012 SLC22A1 (Solute Carrier Family 22 Member 1) isa Protein Coding gene. Plays a critical for elimination of manyendogenous small organic cations as well as a wide array of drugs andenvironmental toxins 79363 RSG1 2.0549338 UP DOWN 0.00735565 0.09173012Differential expression of ABCA1, RSG1 and ADBR2 was replicated inmonocyte gene expression in patients with early onset coronary arterydisease (CAD). These three genes identified expressed differently in CADcases which might play a role in the pathogenesis of atheroscleroticvascular disease. Potential effector of the planar cell polaritysignaling pathway. 7078 TIMP3 2.2165775 UP DOWN 0.0200527 0.09173012TIMP3 blocks the binding of VEGF to VEGF receptor-2 and inhibitsdownstream signaling and angiogenesis. This property seems to beindependent of its MMP- inhibitory activity, indicating a new functionfor this molecule. Complexes with metalloproteinases (such ascollagenases) and irreversibly inactivates them by binding to theircatalytic zinc cofactor. Diseases associated with TIMP3 include SorsbyFundus Dystrophy and Pseudoinflammatory 131601 TPRA1 2.0833867 UP DOWN0.01276787 0.09173012 TPRA1 whose physiological functions are unknown,was first cloned as a GLP-1 receptor homolog in 3T3-L1 adipocytes and isalso expressed in tissues whose development requires Hh signaling,including heart, brain, lung, pancreas, and muscle 752014 CEMP12.0399396 UP DOWN 0.01435211 0.09173012 CEMP1 (Cementum Protein 1) is aProtein Coding gene. Diseases associated with CEMP1 include Coccidiosis.79058 ASPSCR1 2.0533528 UP DOWN 0.01435211 0.09173012 ASPSCR1 encodes aprotein that contains a UBX domain and interacts with glucosetransporter type 4 (GLUT4). This protein is a tether, which sequestersthe GLUT4 in intracellular vesicles in muscle and fat cells in theabsence of insulin, and redistributes the GLUT4 to the plasma membranewithin minutes of insulin stimulation 113655 MFSD3 2.3885236 UP DOWN0.00672813 0.09173012 Membrane-bound solute carriers (SLCs) areessential as they maintain several physiological functions, such asnutrient uptake, ion transport and waste removal. The SLC familycomprise about 400 transporters, and two new putative family memberswere identified, major facilitator superfamily domain containing 1(MFSD1) and 3 (MFSD3) 256236 NAPSB 2.6573431 UP DOWN 0.016107270.09173012 NAPSB is a pseudogene. Although not fully functional,pseudogenes may be functional, similar to other kinds of noncoding DNA,which can perform regulatory functions. 55655 NLRP2 2.3330774 UP DOWN0.01434127 0.09173012 NLRP2 suppresses TNF- and CD40- induced NFKB1activity at the level of the IKK complex, by inhibiting NFKBIAdegradation induced by TNF. When associated with PYCARD, activatesCASP1, leading to the secretion of mature proinflammatory cytokine IL1B.May be a component of the inflammasome, a protein complex which alsoincludes PYCARD, CARD8 and CASP1 and whose function would be theactivation of pro- inflammatory caspases. 25807 RHBDD3 2.2666128 UP DOWN0.02240318 0.09841397 Rhbdd3, a member of the rhomboid family ofproteases, suppressed the activation of DCs and production ofinterleukin 6 (IL-6) triggered by Toll- like receptors (TLRs). Rhbdd3-deficient mice spontaneously developed autoimmune diseases characterizedby an increased abundance of the TH17 subset of helper T cells anddecreased number of regulatory T cells due to the increase in IL-6 fromDCs″

PBMC-GEP Prediction Model Development

Clinical Profiles and Outcome Correlation.

Neither preoperative clinical variables including INTERMACS [4] class,SOFA median score, MELD-XI median score, and Seattle Heart Failure Model(SHFM) nor intra/postoperative clinical variables (except forrespiratory rate) predict Group I versus Group II membership nor year 1survival status. On day 8, 17 patients had organ function improvement(Group I) and 12 patients had no organ function improvement (Group II),with one died on postoperative day 3. Nine patients in INTERMACS class1-2 preoperatively improved at day 8, while 8 patients did not improve(Fisher's Exact Test p<0.005). Eight patients in INTERMACS class 3-4improved while 4 did not improve (FIG. 6). The inefficiency of clinicalscores in correlating with OD in critically ill AdHF patients [10]supports our rationale in developing a preoperative biomarker predictiontest.

Prediction of Early Postoperative Organ Function Changes.

We built a model using the SVM algorithm by randomly selecting 20samples out of 29 total, stratified by membership in Group I versusGroup II. To test the model, the remaining 9 samples were stratified bymembership in Group I or Group II. An average prediction accuracy of 93%(range: 78-100%) was achieved after running the model building process25 times (Table 6).

TABLE 6 Prediction of Organ Function Improvement Group I vs II. RunAccuracy % PM1 100 PM2 100 PM3 100 PM4 89 PM5 89 PM6 78 PM7 100 PM8 89PM9 89 PM10 89 PM11 100 PM12 100 PM13 100 PM14 100 PM15 89 PM16 100 PM1789 PM18 100 PM19 100 PM20 89 PM21 89 PM22 100 PM23 100 PM24 89 PM25 89Average 94

Out of 29 samples, 20 were randomly selected, stratified by membershipin Group I or Group II, were used to build the model and the remaining 9samples were used to test the model.

Rt-Qpcr Validation.

To validate the NGS results in this study, we performed a limitedRT-qPCR experiment to assay the 4 highest ranked genes (by statisticalsignificance and correlation between Group I and Group II expressionlevels). Results show that 2 out of 4 genes (KIR2DL4, BATF2)concordantly correlated between NGS and RT-qPCR expression levels,showing downregulation in Group I and upregulation in Group II. Those 2genes therefore might become candidates for the prognostic testdevelopment. The RT-qPCR results of ANKRD22 and NAPSA expression levelshowed an equivocal relationship to the NGS results. We attributed thisdiscrepancy to the difference in method. This result is in agreementwith the internal validation during the Allomap™ test development, inwhich 68 out of 252 candidate genes discovered by high-throughputtechnology were confirmed by concordant expression changes usingRT-qPCR. Therefore, these 68 genes were retained for further Allomap™test development.

FIGS. 3A-3B. illustrate organ function and outcomes. FIG. 3A shows organfunction and outcomes of 29 patients across five time points. Out of 29AdHF-patients undergoing MCS-surgery, 17 patients had organ functionimprovement from preoperative day −1 (TP1) to day 8 (TP5) (Group I) and12 patients had no organ function improvement (Group II). Each blackline represents one 1-year survivor while each red line represents one1-year non-survivor. In each group, non-survivors are shown in red. FIG.3B shows that, out of 29 AdHF-patients undergoing MCS-surgery, 17patients improved (Group I, upper right quadrant) and 12 patients didnot improve (Group II, remaining 3 quadrants) from day −1 (TP1) to day 8(TP5). Each large dark bullet represents one patient who died within oneyear. Absence of improvement of either score was associated with reduced1-year survival.

FIG. 4 show the Kaplan-Meier 1-year survival in Group I vs. Group II. Inthe 17 patients who improved (Group I=Functional recovery=Organ functionimproving=Low Risk) vs. the 11 patients who did not improve (Group II=Nofunctional recovery=Organ function not improving=High Risk), thetime-to-event Kaplan-Meier survival analysis suggested that thesignificantly elevated risk (log rank test p=0.00182) of death in GroupII continued over the 1-year period following MCS-surgery.

FIGS. 5A-5C show overlap of significant genes associated with organfunction improvement and survival benefit. FIG. 5A shows hierarchicalclustering of significant genes day −1 (TP1). Left: The Volcano plot of28 genes, which are differentially expressed between Group I and GroupII. Right: Hierarchical clustering of the 28 candidate genes for theprediction test demonstrates the differential gene expression betweenGroup I and Group II. FIG. 5B shows hierarchical clustering of genesassociated with survival benefit. Left: The Volcano plot of 105 genes,which are differentially expressed between Group I and Group II. Right:Hierarchical clustering 17 of the 105 candidate genes for the predictiontest demonstrates the differential gene expression between GroupI=Survival, Group II=Non-survival. FIG. 5C shows overlap genes from bothimprovement group and 1-year survival outcome. Left: Venn-Diagram showsthe 28 DEGs identified in the comparison by Improvement Score (red) andthe Right shows the 105 DEGs identified by comparing 1-Year Survival(blue). 12 DEGs were shared across the two comparisons. Right: The 12overlap genes.

FIG. 6 shows an exemplary prediction biomarker development rationale.Preoperative clinical heart failure/organ function severity scores(INTERMACS class, SOFA median score, MELD-XI median score) anddemographics (age, gender) did not reliably discriminate postoperativeorgan function improvement (ROC, 95% confidence interval) and long termsurvival (Cox Proportional Hazard Model, 95% confidence interval). Incontrast, the PBMC-GEP correlates well with postoperative organ functionimprovement and long term survival.

Discussion

We present data to support our hypotheses that in AdHF patientsundergoing MCS implantation, preoperative differential PBMC-GEP areassociated with and are predictive of early postoperative SOFA andMELD-XI score changes. We defined these clinical parameters as thedifference in score between one day before surgery and 8 days aftersurgery as a surrogate marker for long-term mortality risk. Our studiesshow the set of 28 genes derived from preoperative PBMC GEP ispredictive of early postoperative improvement or non-improvement of SOFAand MELD-XI scores. Out of the 28 preoperative genes, the following 12genes are of specific biological interest due to their overlap indifferentiating early postoperative organ function improvement and year1 survivor status.

Potential Biological Implications of Overlapping Genes

Hypothetical mechanisms of up-regulated genes in non-improvement of SOFAscore and MELD-XI score and year 1 non-survivors.

BATF2 belongs to a class of transcription factors that regulate variousimmunological functions and control the development and differentiationof immune cells. Functional studies demonstrated a predominant role forBATF2 in controlling Th2 cell functions and lineage development of Tlymphocytes. Following infection, BATF2 participates in the developmentof and differentiation of CD8 (+) thymic conventional dendritic cells inthe immune system [47]. BATF2 plays a key component involved in IFNsignaling and positive regulation of immune responses by alteringexpression of cytokines and chemokines. Therefore, it possibly maintainsthe balance in inflammatory processes. BATF2 is an essentialtranscription factor for gene regulation and effector functions inclassical macrophage activation [48]. AGRIN is a gene with a ubiquitousrole and is evolutionarily conserved in the extracellular matrix (ECM)[49]. Its intracellular processes include proliferation, apoptosis,migration, motility, autophagy, angiogenesis, tumorigenesis, andimmunological responses [50, 51]. AGRIN interacts with theα/β-dystroglycan receptor in the formation of immunological synapseswith lymphocytes and aids in activation [52] as well as maintainingmonocyte cell survival downstream in an α-dystroglycan dependent manner[53]. The AGRIN LG3 domain has been used as a biomarker for detection ofprematurely ruptured fetal membranes [54]. ANKR22, involved in the lipidmodification of proteins [55], has been patented as a possible biomarkerfor several types of cancers [56, 57] to identify patient responses tocancer immunotherapy. FRMD6 has been linked to various complex diseases,such as asthma, Alzheimer's disease, and lung cancer. It plays acritical role in regulating both cell proliferation and apoptosis, whereit is thought to have tumor suppressor properties. FRMD6 may helpmediate the process by which Vitamin D inhibits the proliferation ofimmune cells [58, 59]. Upregulation of FRMD6 has been suggested as aprognostic marker in colorectal cancer [59]. KIR2DL4 codes fortransmembrane glycoproteins expressed by natural killer (NK) cells andsubsets of T cells. KIR2DL4 inhibits the activity of NK cells and mayreduce activation induced cell death in these T cells in Sézary syndrome[60], [61, 62]. KIR2DL4 is an unusual member of the KIR family thatrecognizes human leukocyte antigen G and mediates NK-cell activation[63] and has been suggested as a useful diagnostic biomarker ofneoplastic NK-cell proliferations [64].

Hypothetical mechanisms of down-regulated genes in non-improvement ofSOFA score and MELD-XI score and year 1 non-survivors.

SAP25 is a member of the nucleocytoplasmic shuttling proteins that arelocated in promyelocytic leukemia (PML) nuclear bodies. PML nuclearbodies are implicated in diverse cellular functions, such as generegulation, apoptosis, senescence, DNA repair, and antiviral response[65], [66, 67]. NAPSA is a pronapsin gene, which may have a considerablediagnostic value as a marker for primary lung cancer. NAPSA was detectedin a subset of poorly differentiated papillary thyroid carcinomas andanaplastic carcinomas [68]. TIMP3 is an extracellular matrix-boundprotein, which regulates matrix composition and affects tumor growth.TIMP3 suppresses tumor inactivation in cancer by mechanisms of invasionand angiogenesis [69]. TIMP-3 downregulation is associated withaggressive non-small cell lung cancer and hepatocarcinoma cells, ascompared with less invasive and/or normal lung and liver cells [70]. Itmediates vascular endothelial growth factor (VEGF) by blocking thebinding of VEGF to VEGF receptor-2, inhibiting downstream signaling, andprevents angiogenesis. These inhibitive properties seem to beindependent of its matrix metalloproteinases (MMP)-inhibitory activity,which indicates a new function for this molecule. RHBDD3 is a member ofthe rhomboid family of proteases that suppresses the activation ofdendritic cells (DCs) and production of interleukin 6 (IL-6) triggeredby Toll-like receptors. The rhomboid proteins are involved in signalingvia the receptor for epidermal growth factor, mitochondrial homeostasisand parasite invasion [71, 72]. RHBDD3 negatively controls theactivation of DCs and maintains the balance of regulatory T cells andTH17 cells by inhibiting the production of IL-6 by DCs, thuscontributing to the prevention of autoimmune diseases [72].

In summary, our central postulate is that OD and death after MCS- orHTx-surgery results from innate and adaptive immune cell dysfunction.Therefore, leukocyte immune-biology information may be used to develop apreoperative test, which more precisely predicts postoperative outcomesin the individual AdHF-patient. To meet this clinical goal, we havedeveloped a novel concept of FRP, which is based on our assessment thatthe key prognostic information is the preoperative potential topostoperatively restore an equilibrium rather than the absolutemagnitude of preoperative OD. In this clinical context, we interpret thepotential biological role of the 12 overlap genes as follows: wehypothesize BATF2 is chronically more activated in GROUP IIAdHF-patients in comparison to GROUP I patients. BATF2 activation is dueto its attempts to repair the cell necrosis-mediated damage caused byOD. This hyper-activation leads to exhaustion of adaptive immunitycells, which may explain the protracted time-course-to-death in Group IIpatients. (FIG. 4). To garner support for this hypothesis, we haveinitiated a study that incorporated multiplex flow cytometry markers,cell free methylated DNA, and mitochondrial DNA into the study protocol.For RHBDD3, its downregulation in patients with rheumatoid arthritis,ulcerative colitis and Crohn's disease [72] may be beneficial inpreventing auto-immune aggression. However, its down-regulation inAdHF-patients undergoing MCS-surgery might exacerbate an inappropriateinnate inflammatory response and inappropriate adaptiveimmune-incompetence via a less inhibitory effect on the IL6-pathway[73]. Furthermore, it is interesting to note that upregulation of genes,such as ANKRD22, FRMD6, and KIR3DL2, and down-regulation of genes, suchas TIMP3, SAP25, NAPSA, and TIMP are associated with a worse prognosisin cancer, are also associated with a worse prognosis in AdHF. Thisraises the question about common pathways in both clinical syndromes.

Health System Implication Perspectives.

Our data suggest that the preoperative dynamic recovery potential,rather than the static severity of OD, is the key prognostic property torestoring equilibrium after surgery. This also presents the possibilityof using a preoperative blood sample to identify AdHF-patients who mayhave a high chance of early postoperative recovery and a potentiallygood long-term prognosis. If the preoperative blood test result predictsa high FRP (Group I), this data might lead to the recommendation toundergo surgery. If the preoperative blood test suggests a low FRP(Group II), the healthcare team may avoid a potentially harmfulrecommendation of surgery at that time. In the US, we estimate that outof 30,000-60,000 individuals per year with AdHF and potential candidatesfor MCS, at least 7,500-15,000 might not benefit from undergoing surgerybased on the test results if they are too sick to benefit from MCSsurgery. Since HF is a major public health concern due to its tremendoussocietal and economic burden, with estimated costs in the U.S. of $37.2billion in 2009 and with expectations to increase to $97.0 billion by2030, our proposed prediction test would simultaneously allow to tailorthe individual patient's personal benefits and also enhancecost-effectiveness in U.S. healthcare.

The clinical decision-making challenge at the time of AdHF evaluationoften culminates in the choice between modern medicine and compassionateend of life care. This ultimate scenario is demanding medically,ethically and economically. It deserves the best evidence-based decisionmaking support that personalized precision medicine research has tooffer that lives up to the highest humanistic expectations that societyentrusts us with.

Limitations.

First, our outcome parameter in this proof-of-principle study used adichotomous endpoint (Improvement versus No Improvement of organfunction on day 8 postoperatively). In a planned expansion of the studyto include a larger cohort, we will treat the outcome parameter as aquantitative continuous variable. Second, we have not incorporatedmultisystemi level protein markers into our analysis. In a plannedextension of the project, we will include multiplex flow cytometry andcytokine parameters. Third, the study had a small sample size. Thisposes inherent limitations on Group I vs Group II comparisons. Thelogistic regression/Cox-PH models were constructed with only onepredictor variable each due to sample size constraints. We also reportedthe coefficients/accuracy measures from these models with 95% confidenceintervals, which properly reflect our uncertainty about the parameterestimates as a function of sample size. Fourth, the RT-qPCR validationwas limited by a lack of biological material necessary to complete thetest. We will expand this validation to include all candidate genes in afollow-up study. Fifth, as in translational biomarker development ingeneral, many results were a consequence ofoperator/researcher-dependent decisions. Sixth, while we chose to baseour analysis on AdHF-patients undergoing MCS-surgery alone to addressthe problem of MCS-related perioperative MOD [4, 33, 34], we acknowledgethat we have not addressed aspects of the PBMC-biology related toMCS-surgery intervention versus general heart surgery. In order toaddress this question, we have initiated a follow-up project examiningAdHF-cohorts undergoing OMM, HTx, coronary artery bypass surgery,percutaneous coronary interventions, valve replacement, valve repair,arrhythmia interventions and healthy volunteers, utilizing the samestudy protocol. These results will be reported separately.

Conclusions

In AdHF patients undergoing MCS implantation, the postoperative clinicalimprovement of OD within one week of surgery is associated with reducedlong-term mortality and a PBMC GEP that differs from that of patientswho do not improve, is already present preoperatively and may lenditself to outcome prediction. The underlying mechanisms and prognosticimplications to improve patient outcomes warrant further study in largerlongitudinal cohorts.

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Example 2: Peripheral Blood Transcriptome Biomarker Test to DiagnoseFunctional Recovery Potential in Advanced Heart Failure

This Example illustrates the outcome prediction obtained by use of theFunctional Recovery Potential (FRP), which refers to the potential torecover from stressors based on chronological age and multiple otherfactors, including primary and secondary organ failure, comorbidities,frailty, disabilities.

Heart failure (HF) is a complex clinical syndrome that results from anystructural or functional cardiovascular disorder that causes systemichypoperfusion and failure to meet the body's metabolic demands. HF isinitiated by various myocardial injury mechanisms. Despite chronicneurohormonal upregulation in order to maintain a compensated state,further myocardial injury leads to HF progression, resulting in overallcatabolic/anabolic imbalance, secondary organ dysfunction, cardiaccachexia, iron deficiency anemia, and frailty[3]. This triggers immunesystem activation which coincides with progressive dysfunction of thekidneys, liver, bone marrow, brain and metabolism, creating a milieusimilar to systemic diseases[4-6], clinically presenting as advanced HF(AdHF) with severely limited prognosis. Outcomes are dependent onHF-severity, but also on chronological age (CA) and multiple otherfactors jointly called the “Personal Biological Age (PBA)”. At any givenCA, there are great biological disparities and great heterogeneity inhealth outcomes[7,8]. This discrepancy relates to a difference in thepotential of individual persons to recover from stressors termed theFunctional Recovery Potential (FRP), or in equivalent term, theprobability of Functional Recovery (FR). Our central postulate is thatFRP integrates the clinical composite including CA as well as PBA(primary and secondary organ failure, comorbidities, frailty,disabilities) (FIG. 7).

AdHF patients with low FRP may be at increased risk for death afterAdHF-therapies such as mechanical circulatory support (MCS) or hearttransplantation (HTx). As described in Example 1, preoperativedifferential gene expression profiles (GEP) of peripheral bloodmononuclear cell (PBMC) are predictive of early postoperative outcomesin AdHF patients undergoing MCS. We defined FRP outcomes as changes ofSequential Organ Failure Assessment (SOFA) and Model of Endstage LiverDisease except INR (MELD-XI) score from preoperatively to 8 dayspostoperatively, which correlates with long term mortality[9].

FRP is a generalizable and clinically useful concept and can be: 1)defined as a person's potential to return to a functional life, afterstressor exposure; 2) modulated by long-term bio-psycho-socialinterventions; 3) characterized within a general clinical frameworkintegrating CA and PBA data; 4) quantitatively described; 5) used as asurrogate for long-term outcome prediction; and 6) diagnosed frompre-stressor molecular data. Incorporating these molecular data in theclinical encounter can improve the quality of the decision-making in ashared decision-making process and help achieve the value-basedhealthcare goals of optimizing patient experience, minimizing morbidityand mortality outcomes and maximizing health system cost-effectiveness.In this Example, we discuss the biomedical foundation of FRP andpotential clinical utility in HF medicine.

AdHF Outcomes in High-Risk AdHF Patients

During the last five decades, HF outcomes have improved with medicalmanagement[1,2,10]. However, patients with stage D, or AdHF, oftencannot tolerate optimal medical management (OMM) by guideline directedmedical therapy (GDMT) and do not derive the same benefit as patientswith less advanced disease[11]. Older patients may not derive the samebenefit as younger patients[12].

It has been suggested that biomarker-guided therapy could improveoutcomes over solely GDMT[13]. We describe a biomarker to assist theclinician in predicting long-term outcomes afterAdHF-surgical/interventional therapies.

Outcomes in AdHF Patients Undergoing Revascularization

For AdHF patients undergoing high risk percutaneous coronaryintervention (PCI), the benefits or harms of PCI in HF populations areunknown because of a lack of randomized trials [14].

For AdHF patients undergoing high risk coronary artery bypass surgery(CABG), there is limited information regarding efficacy in different agegroups. In the Surgical Treatment for Ischemic Heart Failure (STICH)Study trial, a total 1,212 patients with an left ventricular ejectionfraction (LVEF) of <35% were randomly assigned to undergo CABG plusmedical therapy or medical therapy alone[15,16]. In the SurgicalTreatment for Ischemic Heart Failure Extended Study (STICHES) trial, themedian duration of follow-up was 9.8 years. There was a trend towards asmaller reduction in all-cause mortality with CABG compared to GDMT inolder compared with younger patients, implying that an improvedunderstanding of the efficacy of CABG in different age groups isneeded[14,17]. This result is consistent with recent HF trials[12].Since there were few patients in the older age groups, the truelong-term benefit may be even lower. i.e. there is equipoise betweenGDMT and CABG in patients >67 years with Heart failure with reducedejection fraction (HFrEF)[14] (Table 7).

Outcomes in AdHF Patients Undergoing Valve Interventions

AdHF Patients Undergoing Transcatheter Aortic Valve Replacement (TAVR).

Following the initial TAVR experience[18,19] (Table 7), mortality in USclinical practice at 1-year follow-up was 23.7%. It is “imperative tofocus on better prediction of the overall risks and benefits of theprocedure, particularly given the existing comorbidities of the group ofpatients being considered for TAVR.”[20] (Table 7). In a systematicreview on TAVR outcomes, 46.4% and 51.6% of deaths were related tonon-cardiovascular causes within and after the first 30 days,respectively[21]. In the Intermediate Risk TAVR trial[22] (Table 7), theguideline for patient inclusion was an Society of Thoracic Surgeons(STS) risk score [102] and EuroSCORE [103], based on the presence ofcoexisting illnesses to predict mortality at 30 days, between 4-8%[23].The main results showed that TAVR was not inferior to surgery withrespect to outcomes at 2 years (death from any cause or disablingstroke).

Per 2017 recommendations, the risks of death and morbidity associatedwith the natural history of severe aortic valve stenosis need to beweighed against the risk related to aortic valve replacement as a basisfor recommendation of treatment[24,25]. TAVR is not recommended inpatients in whom existing comorbidities would preclude the expectedbenefit from correction of aortic stenosis (AS)[26].

AdHF Patients Undergoing MitraClip.

The overall mortality rate after surgical repair of functional mitralregurgitation (FMR) ranges from 20% to 50%[27-29]. Mitra-Clip therapy isan emerging option for selected high-risk patients with FMR[30,31]. TheHigh Risk Study, an arm of the EVEREST II trial, enrolled symptomaticpatients with 3+ to 4+ MR for whom surgical risk for perioperativemortality rate was estimated to be 212%, using the STScalculator[32,33]. Potentially qualifying criteria included high-riskpatients with porcelain aorta, mobile ascending aorta atheroma,post-mediastinal radiation, functional MR with left ventricular ejectionfraction (LVEF) <40%, age older than 75 years with LVEF <40%, previousmedian sternotomy with patent bypass graft(s), >2 previous chestsurgeries, hepatic cirrhosis, or 23 of the following STS high-riskcriteria: creatinine level >2.5 mg/dl, previous chest surgery, age olderthan 75 years, or LVEF <35%[34] (Table 7). A significant number ofpatients with symptomatic MR have extensive comorbidities or uncertainindications for surgery and are defined as high surgical risk,inoperable or not indicated for surgery, and approximately one-half ofpatients with symptomatic severe MR may not undergo surgery. In a recentMitra-Clip-meta-analysis, one-year mortality rate was 16% (408/2498) andsimilar among groups in patients with FMR vs degenerative mitralregurgitation (DMR). The authors conclude that better patient selectionand performing percutaneous edge-to-edge repair at earlier stage couldavoid treatment of those patients with advanced LV remodeling, more thansevere MR, and many comorbidities, who benefit less from theprocedure[35] (Table 7).

Per 2017 American College of Cardiology (ACC)/American Heart Association(AHA) recommendations, transcatheter mitral valve repair may beconsidered for severely symptomatic patients (New York Heart Association(NYHA) class III to IV) with chronic severe primary MR (stage D) whohave favorable anatomy for the repair procedure and a reasonable lifeexpectancy but who have a prohibitive surgical risk because of severecomorbidities and remain severely symptomatic despite optimal GDMT forHF[24].

Outcomes in AdHF Patients Undergoing Ventricular Tachyarrhythmia (VT)Interventions

AdHF Patients Undergoing Internal Cardioverter Defibrillator (ICD)Device Therapy.

Patients with stage D heart failure are at increased risk of suddencardiac death (SCD) from ventricular tachyarrhythmia, thusanti-arrhythmia device therapy is an integral part of their management.Introduction of ICD for primary prevention of sudden cardiac death wasproven to be of great benefit with reduction in mortality of 31% in 20months in patients with history of myocardial infarction (MI) andEF<30%[36]. Furthermore, in patients with EF<35% regardless of etiologyand mild to moderate symptoms, ICD implantation decreases mortality by23% over 5 years [40] [Bardy 2005].

ACC/AHA heart failure guidelines recommend ICD implantation in allpatients with ejection fraction of <30% and NYHA class I symptoms and inthose with EF<35% with NYHA Class II and III symptoms[2]. However, thistherapy is reserved for patients with projected survival of more thanone year, which precludes some of the patients with very advanceddisease from receiving an ICD. In octogenarians who are due for an ICD,careful thought should be given to the current clinical status,comorbidities, and general frailty prior to considering them for theprocedure[38]. Goldenberg et al. highlighted a U-shaped relationshipbetween the severity of heart failure and mortality benefit from ICDtherapy[39].

AdHF Patients Undergoing BVPM-Device Therapy.

Cardiac resynchronization therapy (CRT) in patients with wide QRScomplex and Left Bundle Branch Block (LBBB) patter has led toimprovement of ventricular contractility and EF, reduction in secondarymitral regurgitation, reversal of remodeling and decrease in mortality.However, around 30% of individuals receiving this therapy derive nobenefit or experience worsening of their symptoms[40]. Similar to ICD,patients with stage D HF are often considered to be too sick to benefitfrom CRT and therefore their treatment is limited to advanced therapies(MCS and Htx) or palliative care[2].

AdHF Patients Undergoing VT-Ablation Therapy.

Ventricular tachycardia (VT)-ablation therapy has increased in the US,specifically in patients worsening clinical risk profile including ageand comorbidity burden[41]. In a contemporary registry, catheterablation of VT in patients with structural heart disease results in 70%freedom from VT recurrence, with an overall transplant and/or mortalityrate of 15% at 1 year. Patients who died or underwent transplant wereolder and had higher rates of hyperlipidemia, diabetes mellitus, atrialfibrillation, chronic kidney disease, advanced heart failure, ICD, CRT,lower EF, electrical storm (ES), shocks, amiodarone, and ≥2antiarrhythmic drugs. In the Cox multiple regression frailty analysis,transplant or death was associated with older age, NYHA class III andIV, chronic kidney disease, electrical storm, and use of hemodynamicsupport devices[42] (Table 7). The International Ventricular TachycardiaCenter Collaborative Study Group registry of 2,061 patients whounderwent VT ablation analyzed survival of patients 270 years with andwithout VT recurrence. Of 681 patients, 92% were men, 71% had ischemicVT, and 42% had VT storm at presentation. LVEF was 30±11%. Compared withpatients <70 years, patients ≥70 years had higher 1-year mortality (15%versus 11%; P=0.002)[43] (Table 7). Patients with electrical storm areamong the highest risk VT populations because they are frailer, older,with a lower LVEF, more advanced heart failure status, and morecomorbidities. A comprehensive approach needs to include not only thearrhythmia ablation but also careful treatment of the comorbidities,such as advanced heart failure, hypertension, hyperlipidemia, atrialfibrillation, diabetes, and chronic kidney disease[44]. A majorchallenge of VT ablation is hemodynamic intolerance of the inducedarrhythmia, with as few as 10% of induced arrhythmias being stable[45].Extracorporeal membrane oxygenation (ECMO) will be increasingly used inthis scenario[46]. The challenge is to predict a prohibitively high riskof not being able to wean the patient from VA-ECMOpost-interventionally.

Outcomes in AdHF Patients Undergoing MCS/HTx

AdHF Patients Undergoing MCS.

MCS devices, originally used for patients with AdHF as abridge-to-transplant or bridge-to-recovery, now increasingly used asdestination (lifelong) therapy, have the potential to outnumber HTx by afactor of 1:10[47]. Because of this success, destination MCS isincreasingly being offered to patients with challenging clinicalprofiles. There is significant patient-to-patient variability for riskof adverse events. Overall survival continues to remain >80% at 1 yearand 70% at 2 years[48] (Table 7).

AdHF Patients Undergoing Heart Transplantation.

Since its first introduction in 1967, heart transplantation (HTx) offersan unparalleled survival benefit in select patients with stage D HF, andremains the gold standard of treatment Stage D HF is defined asrefractory HF and often accompanied by the following parameters:repeated (>2) hospitalizations or emergency department visits for HF inthe past year, worsening renal function, unintentional weight loss >10%(cardiac cachexia), intolerance to medical therapy due to hypotensionand/or worsening renal function, persistent dyspnea/fatigue,hyponatremia and escalating use of diuretics (>160 mg/d and/or use ofsupplemental metolazone therapy) and frequent ICD shocks.

Annually, there are approximately 3,000 HTx performed in the U.S. andthe number of donors have remained steady for decades. Current graftsurvival rates with advances in immunosuppressive therapy are 85-90%,75-80%, and 70-75% in adults at 1-, 3-, 5-year respectively, and amedian survival of 11-13 years. Internationally, contemporary mediansurvival after adult heart transplantation is 10.7 years[49] (Table 7).

ACC/AHA guidelines designates a class I indication for hearttransplantation only in carefully selected patients with stage D HFdespite GDMT, device, and surgical management. The leading cumulativecauses of death are graft failure, infection, cancer, and multiple organfailure.

TABLE 7 Summary of AdHF-intervention studies with inclusion criteria,sample size and major outcomes: Across the different interventions, the1-year mortality rate is in the range of 10-30%. Author Inter- Outcome/(Year) Inclusion Patients vention Comments Petrie 2016 LVEF <35% 1,212CABG >67 y equipoise GDMT vs CABG at 10 y Holmes 2015 STS7% 12,182 TAVR1 y mortality 23% Leon 2010 STS >15-50% 2,032 TAVR 1 y mortality 30%Smith 2011 STS >10-15% 699 TAVR 1 y mortality 24% Leon 2016 STS >4-8%2,032 TAVR 1 y mortality 12% Whitlow 2012 STS ≥12 78 Mitra- 1 ymortality 24% Clip Chiarito 2018 LVEF39-59% 2,615 Mitra- 1 y mortality16% Clip Tung 2015 LVEF31% 2,061 VT- 1 y mortality 12% ablation Vakil2017 LVEF30%, >70 y 681 VT- 1 y mortality 15% ablation Vergara 2017LVEF34%, ES 677 VT- 1 y mortality 20% ablation Kirklin 2017 LVEF low17,633 MCS 1 y mortality 20% Lund 2017 LVEF low 21,614 HTx 1 y mortality10-15% Abbreviations: CABG—coronary artery bypass surgery;GDMT—Guideline directed medical therapy; LVEF—left ventricular ejectionfraction; HTx—Heart transplantation; MCS—Mechanical circulatory support;STS—Society of Thoracic Surgeons; TAVR—Transcatheter Aortic ValveReplacement; VT—Ventricular tachycardia; Y—yearOutcome Prediction Biomarker Prototype

In our proof-of-principle outcome prediction biomarker prototype studydescribed in Example 1, our central postulate is that OD and patientdeath after MCS- or HTx-surgery results from innate and adaptive immunecell dysfunction. Therefore, our goal was to use leukocyteimmune-biology information to develop a preoperative test, which wouldprecisely predict postoperative outcomes in the individual AdHF patient.We utilized the widely accepted SOFA[72] and MELD-XI [67,73,74] scoresas quantitative assessment tools to interpret the PBMC data and todevelop a predictive leukocyte biomarker. We specifically hypothesizedthat one of the most significant clinical outcome parameters for AdHFpatients undergoing MCS is the probability of organ function improvementfrom one day before to eight days after surgery. Therefore, patientswere grouped into two organ failure risk strata: Group I=improving (bothSOFA and MELD-XI scores improve from day −1 to day 8) and Group II=notimproving (SOFA and/or MELD-XI score(s) do not improve from day −1 today 8). In other words, if the MCS-surgery improves the hemodynamicsituation without complications, then the patient's organ function isexpected to recover by postoperative day 5 and clearly by postoperativeday 8, which should be reflected in a concordant improvement of SOFA andMELD-XI score, from day −1 to day 8. On the other hand, if SOFA orMELD-XI, or both, scores do not improve from day −1 to day 8, wehypothesize that this problem may potentially impact long-term survival.We hypothesized that in AdHF patients undergoing MCS-surgery, HF-relatedpreoperative PBMC GEP correlate with and predict changes of earlypostoperative organ function status as surrogates for 1-year survival.Our studies showed the set of 28 identified genes [201] derived frompreoperative PBMC GEP is predictive of early postoperative improvementor non-improvement of SOFA and MELD-XI scores. Out of the 28preoperative genes, 12 genes were of specific biological interest due totheir overlap in differentiating not only early postoperative organfunction improvement but also year 1 survivor status[9].

Our data suggest that the pre-interventional dynamic recovery potential,rather than the static parameter of “severity of OD”, is the keyprognostic property to restoring equilibrium after surgery. This alsopresents the possibility of using a preoperative blood sample toidentify AdHF-patients who may have a high chance of early postoperativerecovery and a potentially good long-term prognosis. If the preoperativeblood test result predicts a high FRP (Group I), this data might lead tothe recommendation to undergo surgery. If the preoperative blood testsuggests a low FRP (Group II), the healthcare team may avoid apotentially harmful recommendation of surgery at that time. In the US,we estimate that out of 30,000-60,000 individuals per year with AdHF andpotential candidates for MCS and other AdHF-surgical/interventionaltherapies, at least 7,500-15,000 might not benefit from undergoing theintervention based on the test results if they are too sick at the timeof testing. Since HF is a major public health concern due to itstremendous societal and economic burden, with estimated costs in theU.S. of $37.2 billion in 2009 and with expectations to increase to $97.0billion by 2030, our proposed prediction test would simultaneously allowto tailor high-tech modern medicine to the individual patients needs,i.e. optimize personal morbidity and mortality benefits and personalexperience while also enhancing cost-effectiveness in U.S. healthcare.This concept would contribute to the advancement of highvalue-healthcare and reduction of low-value-healthcare.

It is important for the patient to choose the therapeutic option withthe best short-, medium- and long-term outcome. In order to do so, thedoctor needs to be able to predict, from pre-intervention data of thepatient, what the consequences of the different options are. First andforemost, this means that all available pre-intervention data need to beanalyzed for their long-term outcome prediction capacity. None of thecurrent established clinical scoring and prediction tools integrateimmune function parameters [53-59,61-69,72-74,162,163]. They have thetendency to be imprecisely calibrated in estimating risk among severelyill patients [60,61], making the therapeutic recommendation with thebest survival estimate for the individual patient very difficult.Therefore, we intend to develop a molecular blood test that predicts,from pre-intervention data, recovery of organ function and frailtyreversal, which, in turn, predict 1-year survival. This information willhelp tackle the following challenge for the individual patient anddoctor We describe a molecular blood test, based on a PBMC GEP sampletaken 1-3(7) days before undergoing surgical/interventional therapiesfor AdHF, that can assist clinicians in more precisely diagnosing FRP,i.e. predicting FR, as a surrogate marker for 1-year survival and helpthe patient and clinician in the shared-decision making process tochoose the most meaningful treatment option.

Clinical Validity Study

We plan to complete a FDA-clearance Pivotal Trial with ≥1,000 AdHFpatients, stratified for four primary HF-mechanisms (ischemic, overload,arrhythmia, dyscontractility). After completion of a clinical validitystudy of developing the test in a framework of diagnosing the potentialof future organ function recovery and frailty reversal, FDA-clearanceand clinical implementation, we plan to conduct a clinical utilitytrial, testing the impact of adding the test information to the bestcurrent clinical prediction tools of net health outcomes as we did withthe AlloMAP™ test development[164-166]. We plan to make this testcommercially available, likely using the Nanostring platform that hasalready been used for an FDA-cleared In-vitro-Diagnostic MultivariateIndex Assay test[167].

Biomarkers in the Practice of Shared Decision-Making

It is critical to have a multidisciplinary heart team to provideexpertise to make the best recommendation regarding the individualpatient's anticipated benefit [168]. It is important for these teams toget comfortable with the decision to not pursue the most aggressiveoption available in patients for whom the anticipated benefits do notoutweigh the risks. The decision not to offer specificAdHF-surgical/interventional therapies should not be equated withabandoning care [169]. Shared decision-making requires both the patientand the provider to share information, work toward a consensus, andreach agreement on the course of action[170] consistent with thepatient's preferences[171-173]. As we work on technological innovationsto improve the devices, we must also use it responsibly within aframework of care that enables shared decision making and promotespatient goals and well-being [169].

Future Perspectives

We will tailor the molecular test precision medicine results to a highquality Relational Medicine [174] encounter to maximize itseffectiveness. The clinical decision-making challenge at the time ofAdHF evaluation often culminates in the choice between everything modernmedicine has to offer and compassionate end of life care. This ultimatescenario is medically, ethically, and economically demanding. Itdeserves the best evidence-based decision making support thatpersonalized precision medicine research has to offer in order to liveup to the highest humanistic expectations that society entrusts us with.

Over the next decade, this vision of a meaningful practice of modernmedicine will increasingly incorporate the elements of molecularprecision medicine with Relational Medicine, promoting high valuehealthcare over low value healthcare. The monetary have all beenimplemented in the US-healthcare system and are already taking effect.In order to achieve these goals, future generations of healthcareprofessionals will be trained to pursue a practice that allows them toachieve these goals.

References cited in this Example can be found in Deng, M. C., 2018Biomarkers in Medicine Vol. 12(6).

Example 3: Case Studies Show Predictive Value of FRP Scoring

This Example demonstrates the advantages achieved using the predictivevalue of the FRP scoring. Two case studies out of the 29 AdHF-patientsin the Proof-Of-Concept Study illustrate the clinical utility of FRPscoring. Case Study #1 (FIG. 8): MH, a 69-year-old woman, born in 1942,married, who was in the 1970's diagnosed with Dilated Cardiomyopathy,had a “heart attack” in the 1990's, underwent ImplantableCardioverter-Defibrillator (ICD) implantation and BiventricularPacemaker Implantation (BVPM) 1999, had a history of MonoclonalGammopathy of Unknown Significance (MGUS), Diabetes Mellitus (DM) andhypothyroidism. In 2012, she suffered a cardiac arrest, developed renaldysfunction and was hospitalized three times in 12 months for heartfailure decompensation. In July 2012, she was admitted to UCLA incardiogenic shock and multiorgan dysfunction (liver, kidneys, lung,immune system). The AdHF-team was uncertain, but felt that she waslikely approaching end-of-life, and had only a very small chance ofreversing her organ dysfunction in order to be evaluated for advancedheart failure therapies such as MCS or Htx. In contrast to thisassessment, the patient recovered, was eventually being evaluated, sixweeks later underwent destination Heartmate II Left Ventricular AssistDevice (LVAD) implantation and lived a very active life with her husbandthereafter for >5 years. Her preoperative PBMC-GEP (left arrow inFigure) would have indicated—with an accuracy of 93%—a high FRP andtherefore high long-term (1-year) survival probability and would havesupported a proactive strategy recommending an earlier LVAD-surgerytimepoint to the patient. However, this patient's test results were notavailable at the time of shared decision-making.

Case Study #2 (FIG. 8): DB, an 80-year-old man, born in 1933, married, 3kids, biomedical company Ex-CEO, in 1993 suffered a large myocardialinfarction (MI), underwent LAD-PTCA 1993/1997, CABG 2001, ICD 2002, andBVPM 2003. In 2013, his cardiopulmonary exercise capacity was reduced to10 ml/kg/min, and he was evaluated for AdHF-therapy options. Since hewas not a HTx candidate (frailty/age 80), he was offered destination-MCSat UCLA. The patient declined LVAD-surgery for fear of a 10% strokerisk. In July 2014, he was transferred to UCLA from an outside hospitalon intra-aortic balloon pump (IABP), having become more cachectic withtemporal wasting, impending renal and hepatic failure, as well aspneumonia. While the patient now requested destination-LVADimplantation, the AdHF-team was uncertain, but felt that the patient waspossibly too sick for surgery. Ultimately, the team went ahead,implanted the destination Heartmate II LVAD, and the patient died 6weeks later on the respirator and on dialysis on multiorgan failure inthe Cardiothoracic Intensive Care Unit (CTICU). His preoperativePBMC-GEP (right arrow in Figure) would have indicated—with an accuracyof 93%—a low FRP and therefore low long-term (1-year) survivalprobability, and would have supported a palliative strategy,recommending discharge home to allow a dignified dying process in thecontext of the patient's family. However, this patient's FRP testresults were not available at the time of shared decision-making.

Example 4: Treatment of Heart Failure

An individual presents with clinical symptoms of heart failure includingshortness of breath, excessive tiredness, and leg swelling. It isdetermined that the individual has heart failure via diagnostic testsincluding echocardiography, blood tests, electrocardiography, and chestradiography. A blood sample is obtained from the individual and PBMCsare isolated from the blood sample. RNA is isolated from the isolatedPBMCs and subjected to Nanostring analysis to measure gene expression ofRSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA,NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22,BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1,LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF,RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3,SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2,AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY,TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, and FITM1. The geneexpression levels are determined to be elevated or reduced for each ofthese genes and a FRP score is calculated based on these gene expressionlevels. It is determined that the individual as an FRP score of lessthan 5 and is therefore referred for optimal medical management (OMM)and/or palliative care (PC).

A second individual presents with clinical symptoms of heart failure anddiagnostic tests confirm that the individual has heart failure. A bloodsample is obtained from the individual and PBMCs are isolated from theblood sample. RNA is isolated from the PBMCs and subjected to Nanostringanalysis to measure gene expression of RSG1, TPRA1, SAP25, MFSD3, FITM1,SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1,KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5,KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P,KDM5D, EIF1AY, and FITM1. The gene expression levels are determined tobe elevated or reduced for each of these genes and a FRP score iscalculated based on these gene expression levels. It is determined thatthe individual as an FRP score of 7 and is therefore referred fortreatment with mechanical circulatory support (MCS) surgery. Theindividual survives the surgery and the symptoms of heart failure arereduced.

Example 5: Systems Biological Identification of an Age-Related Predictorof Functional Recovery Potential in Advanced Heart Failure

This Example demonstrates that FRP can be improved by additionalclinical and age-related transcriptome data. The Example shows that, inAdHF patients, a model obtained from preoperative data that incorporatesclinical and genomic parameters including genes related to chronologicalage has the ability to predict Group I/II outcomes after MCS surgery.This correlates with long-term outcomes lending itself to outcomeprediction beyond recovery from surgery.

From the study with 29 patients undergoing mechanical circulatorysupport (MCS) surgery, FRP was defined by grouping patients into twoclinically relevant organ failure risk strata: Group I=IMPROVING (SOFAand MELD-XI scores both improve from day −1 to day 8) and Group II=NOTIMPROVING (SOFA and/or MELD-XI score(s) do not improve from day −1 today 8). Peripheral blood mononuclear cell (PBMC) samples were collectedone day before surgery (day −1). Clinical data was collected on day −1and day 8 postoperatively. Purified mRNA was subjected to whole-genomeNext-Generation Sequencing (NGS) analysis. Correlation analyses wereperformed using NGS Strand. Two groups were created by age (60 y): Age A(<60 y, n=13), Age B (≥60 y, n=16). A model was built using thefollowing strategy: Step 1: Clinical model using multivariate logisticregression, Step 2: Transcriptomics model using support vector machine(28 genes transcriptome differentially expressed between Group I/GroupII (Step 2A) and 12 genes based on biological age (Step 2B), and Step 3:Combined Model. This model prediction was proposed to optimize theclinical and transcriptome model.

Out of 29 AdHF-patients undergoing MCS-surgery, 17 patients improved(Group I) while 12 patients did not (Group II). Older patients were morelikely in Group II, i.e. Age B=10/16 (62%) and Age A=2/13 (15%).One-year survival in Group Age I was 10/13 (77%) and in Group Age II8/15 (53%).

The Clinical model, using all clinical parameters as input, identifiedrespiratory rate, chronological age and white blood cell count as thebest clinical combination (cross validation accuracy 82%) to predictGroup I vs Group II. The Transcriptomics model, consisting of the 28previously identified genes (Step 2A) (accuracy 93%) and adding 12age-related genes (Step 2B) (derived from a sub-cohort analysis of oldermale patients) increased the accuracy of prediction model to 94%. Tooptimize the accuracy of prediction, the clinical and transcriptomicmodels were combined to create the Combinatorial Model (accuracy 96%).

Bondar et al., 2017, PLoS One December 13; 12(12) (see Table 1 therein)summarizes the demographics and key clinical data, which can be sortedby age grouping. NICM=nonischemic dilated cardiomyopathy,PPCM=peripartum cardiomyopathy, ICM=ischemic cardiomyopathy,ChemoCM=chemotherapy-induced cardiomyopathy, HM II=Heartmate II,CMAG=Centrimag, LVAD=Left ventricular assist device, RVAD=rightventricular assist device, BVAD=biventricular assist device,HVAD=Heartware LVAD, TAH=Total Artificial Heart, ECMO=extracorporealmembrane oxygenator, GROUP: Organ function changes of SOFA-score andMELD-XI score from preoperative day −1 (TP1) to postoperative day 8(TP5) (Group I=WHITE ROWS=Improvement vs. Group II=GREY ROWS=Noimprovement.

FIG. 3A summarizes the individual patients' organ function improvementand 1-year survival trajectory (discussed further in Example 1 above).SOFA and MELD-XI across five time points (TP) grouped by age (Age A, <60y, Age B, ≥60 y). Each black line represents one 1-year survivor whileeach red line represents one 1-year non-survivor. FIG. 4 showsKaplan-Meier 1-year survival in Age B vs. Age A. The time-to-eventKaplan-Meier survival analysis suggested a trend of elevated risk ofdeath (log rank test p=0.12) in older patients (Age B) continued over a3-6 month period following MCS-surgery.

Table 8 summarizes role of clinical parameters in prediction of FRP.RR-Respiratory Rate; HR-Heart Rate; WBC-White Blood Cell; CB-SerumCreatinine (mg/dl); and AIC-Akaike information criterion. The totalnumber of samples were 29. The multivariate Regression analysis modelwas built on 24 samples and tested on the remaining 5 samples.

TABLE 8 Clinical model building for Group I vs. II membership predictionusing multivariate logistic regression P-value for Variable VariableClinical Variables Removed Removed AIC RR, Age, Sofa Score, Full model —21.42 HR, WBC, CB, Glucose RR, Age, Sofa Score, Glucose 0.8402 19.44 HR,WBC, CB RR, Age, Sofa Score, Heart Rate 0.33447 18.87 WBC, CB RR, Age,Sofa Score, Sofa Score 0.34133 18.29 WBC RR, Age, WBC CB 0.9336 17.47

The prediction model was enhanced using the combinatorial model. Tooptimize the clinical and transcriptomics model, we combined respiratoryrate, chronological age, and White Blood Cell, the 28 genes associatedwith Group I/II (Bondar 2017) and the 12 genes associated withbiological age. This model increased accuracy of Group I/II predictionto 96%.

Example 6: Centralized Testing and Assigning Treatment Regimen

A preserved blood sample is received from a clinician treating anindividual who has been diagnosed with heart failure. RNA is isolatedfrom the blood and subjected to Nanostring analysis to measure geneexpression of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1,NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5,CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1,ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, and FITM1. Thegene expression levels are determined to be elevated or reduced for eachof these genes and a FRP score is calculated based on these geneexpression levels. The FRP score of less than 5 is reported to theclinician with a recommendation for optimal medical management (OMM)and/or palliative care (PC).

Another preserved blood sample is received from a clinician treating anindividual who has been diagnosed with heart failure. RNA is isolatedfrom the blood and subjected to NanoString analysis to measure geneexpression of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1,NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5,CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1,ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, and FITM1. Thegene expression levels are determined to be elevated or reduced for eachof these genes and a FRP score is calculated based on these geneexpression levels. The FRP score of 7 is reported to the clinician witha recommendation for treatment with mechanical circulatory support (MCS)surgery.

Example 7: Kit for Determining Treatment Regimen for Heart Failure

An individual presents with clinical symptoms of heart failure includingshortness of breath, excessive tiredness, and leg swelling. It isdetermined that the individual has heart failure via diagnostic testsincluding echocardiography, blood tests, electrocardiography, and chestradiography. A blood sample is obtained from the individual and PBMCsare isolated from the blood sample. A kit is obtained for isolating RNAis the isolated PBMCs. The kit also contains reagents for Nanostringanalysis to measure gene expression of RSG1, TPRA1, SAP25, MFSD3, FITM1,SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1,KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5,KAL1, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P,KDM5D, EIF1AY, and FITM1. The kit includes software to determine thatthe gene expression levels are elevated or reduced for each of thesegenes and a FRP score is calculated by the software based on these geneexpression levels. The software assigns to the individual an FRP scoreof less than 5 and recommends optimal medical management (OMM) and/orpalliative care (PC).

A second individual presents with clinical symptoms of heart failure anddiagnostic tests confirm that the individual has heart failure. A bloodsample is obtained from the individual and PBMCs are isolated from theblood sample. A kit is obtained for isolating RNA is the isolated PBMCs.The kit also contains reagents for Nanostring analysis to measure geneexpression of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1,NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGR1, KCNH8, CCR8, MME, ETV5,CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1, PRRG1, XIST, RPS4Y1,ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIF1AY, and FITM1. Thekit includes software to determine that the gene expression levels areelevated or reduced for each of these genes and a FRP score iscalculated by the software based on these gene expression levels. Thesoftware assigns to the individual an FRP score of 7 and recommendstreatment with mechanical circulatory support (MCS) surgery. Theindividual survives the surgery and the symptoms of heart failure arereduced.

Throughout this application various publications are referenced. Thedisclosures of these publications in their entireties are herebyincorporated by reference into this application in order to describemore fully the state of the art to which this invention pertains.

Those skilled in the art will appreciate that the conceptions andspecific embodiments disclosed in the foregoing description may bereadily utilized as a basis for modifying or designing other embodimentsfor carrying out the same purposes of the present invention. Thoseskilled in the art will also appreciate that such equivalent embodimentsdo not depart from the spirit and scope of the invention as set forth inthe appended claims.

What is claimed is:
 1. A method for treating an individual sufferingfrom heart failure, comprising: (i) receiving a sample from theindividual; (ii) determining gene expression levels in the sample forDNM1P46 and at least one additional gene comprising RSG1, TPRA1, SAP25,MFSD3, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6,TIMP3, ACVR1C, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGRI,KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5,KALI, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P,KDM5D, EIFIAY, or FITM1; and (iii) providing a treatment to theindividual based on the gene expression levels relative to referencevalues; wherein the treatment is one of the following: (a) optimalmedical management (OMM), palliative care (PC), or a combination thereofwhen the gene expression level of DNM1P46 is upregulated relative to afirst reference value, or (b) a surgical therapy, an interventionaltherapy, or a combination thereof when a gene expression level ofDNM1P46 is equal or downregulated relative to the first reference valueand (1) a gene expression level of at least one of RSG1, TPRA1, SAP25,MFSD3, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, TIMP3,SLC22A1, HEXA-AS1, LOC728431, PDZK1 IP1, ETV5, RANBP17, C6orf164,C7orf50, FADS3, SAC3D1, RBPMS2, CHMP6, SNAPC2, NTSR1, SEPT5, XIST, orFITM1 is upregulated relative to a second reference value, or (2) a geneexpression level of at least one of FRMD6, ACVR1C, KIR2DL4, USP9Y,ANKRD22, BCORP1, HMCN1, GPR63, BATF2, AGRN, CKAP2L, IGSF10, NEGRI,KCNH8, CCR8, MME, CXCL9, HBEGF, DDX43, NEFL, CDCA2, ALDH1A1, OLFM1,FZD4, C15orf38, ST6GALNAC1, SKA1, CD209, AXL, KIR2DL1, KALI, PRRG1,RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, or EIFIAY isdownregulated relative to a third reference value.
 2. The method ofclaim 1, wherein the sample comprises blood, urine, sputum, hair, orskin.
 3. The method of claim 1, wherein each reference value is anexpected expression level value.
 4. The method of claim 1, wherein geneexpression levels in the sample are determined for at least 8 genesselected from the group consisting of RSG1, TPRA1, SAP25, MFSD3, SPTBN5,CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C,DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1,AGRN, CKAP2L, IGSF10, HEXA-AS1, LOC728431, PDZK1IP1, NEGRI, KCNH8, CCR8,ME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2,ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1,CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1,XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, EIFIAY, andFITM1, wherein the at least 8 genes includes DNM1P46.
 5. The method ofclaim 4, wherein the gene expression levels are assigned a score thatcorresponds to the determined expression level of each of the at least 8genes, and wherein the treatment is determined based on the score. 6.The method of claim 5, wherein the score comprises a Function RecoveryPotential (FRP) score.
 7. The method of claim 6, wherein the score isdetermined based on a linear discriminant analysis of data comprisingknown gene expression levels and known FRP scores of a plurality ofindividuals.
 8. The method of claim 1, wherein the treatment is asurgical and/or interventional therapy selected from mechanicalcirculatory support (MCS) surgery, heart transplant (HTx) surgery,coronary artery bypass graft (CABG) surgery, percutaneous coronaryinterventions (PCI), aortic valve replacement (AVR) surgery, mitralvalve replacement (MVR) surgery, trans-catheter aortic valve replacement(TAVR), transcatheter mitral clip, ventricular tachycardia ablation, orstellate gangliectomy.
 9. The method of claim 1, wherein the geneexpression levels are levels determined by polymerase chain reaction(PCR), next generation sequencing (NGS), or other gene expression assayplatform.
 10. The method of claim 1, wherein gene expression levels inthe sample are determined for DNM1P46, BCORP1, HEXA-AS1, BATF2, AGRN,ANKRD22, FRMD6, KIR2DL4, SAP25, NAPSA, TIMP3, and RHBDD3.
 11. The methodof claim 1, wherein the reference values are expression levels ofnormalization genes or expression levels known to be representative ofhealthy individuals and/or individuals known to recover from heartfailure.